The Effect of Social Media on Elections:

Evidence from the United States∗

Thomas Fujiwara† Karsten Müller‡ Carlo Schwarz§

October 25, 2022

Abstract

We study how social media affects election outcomes in the United States. We use variation
in the number of Twitter users across counties induced by early adopters at the 2007 South
by Southwest (SXSW) festival, a key event in Twitter’s rise to popularity. We show that this
variation is unrelated to observable county characteristics and electoral outcomes before the
launch of Twitter. Our results indicate that Twitter lowered the Republican vote share in
the 2016 and 2020 presidential elections, but had limited effects on Congressional elections
and previous presidential elections. Evidence from survey data, primary elections, and a
text analysis of millions of tweets suggests that Twitter’s relatively liberal content may have
persuaded voters with moderate views to vote against Donald Trump.

∗
We are grateful to Pablo Barbera, Levi Boxell, Matt Gentzkow, Ro’ee Levy, Alexey Makarin, Jesse Shapiro, James
Snyder, Ekaterina Zhuravskaya, as well as seminar participants at Princeton University, Imperial College, Warwick
University, Bocconi University, University of California San Diego, Insper, University of São Paulo, Inter-American
Development Bank, IPEG/Pompeu Fabra, University of Zurich, OPESS, Università di Bergamo, the 2021 ASSA
Conference, and the NBER Political Economy Spring 2021 Meeting for their helpful comments.
†
Princeton University, Department of Economics and SPIA, and NBER, fujiwara@princeton.edu.
‡
National University of Singapore, Business School, kmueller@nus.edu.sg.
§
Università Bocconi, Department of Economics and IGIER, and PERICLES, carlo.schwarz@unibocconi.it.
1 Introduction
Does social media affect election outcomes? A popular narrative holds that Twitter played a
decisive role in both recent American presidential elections and the United Kingdom’s “Brexit”
referendum. Many see this as part of social media’s broader influence on political polarization
and the re-emergence of populist politicians in many countries. The U.S. Federal Election
Commissioner, for example, has argued that Facebook “has no idea how seriously it is hurting
democracy” (NPR, 2020a).1
An alternative view suggests that social media platforms are biased against conservatives
(e.g., NPR, 2020b; Wall Street Journal, 2020) and that its younger, relatively left-leaning user
base is unlikely to tilt elections towards right-wing politicians (e.g., Boxell et al., 2017, 2018).
However, there is limited evidence that can be used to evaluate these contrasting (causal)
claims.
This paper focuses on the effects of Twitter, a platform used by almost a quarter of
American adults. We estimate how a county’s number of Twitter users affects election results
by exploiting a persistent network effect sparked by early Twitter adoption, building on
Müller and Schwarz (2019).2 Although it was launched in March 2006, Twitter’s popularity
increased rapidly after its advertising campaign at the South by Southwest festival (SXSW)
in March 2007. The SXSW festival was also key for Twitter’s geographical diffusion: counties
with more SXSW followers who joined during the 2007 festival saw disproportionately higher
growth of Twitter adoption compared to counties with SXSW followers who already joined
before the festival. Consistent with path dependence in technology adoption, this difference
in Twitter use across counties persists.
Our identification strategy leverages the 2007 SXSW festival as a shock to early Twitter
adoption that is uncorrelated with pre-existing election results. Conditional on geographic
controls and previous interest in the SXSW Twitter account, a county’s number of SXSW
followers who joined in March 2007 is essentially uncorrelated with a host of county charac-
teristics. It is also unrelated to election outcomes before Twitter’s launch (going back as far
as 1924) and during the period it had fewer users (between 2006 and 2012). However, the
number of SXSW followers who joined in March 2007 is correlated with Twitter usage in
2016, and has predictive power for the 2016 and 2020 presidential election results.
We estimate that a 10% increase in a county’s number of Twitter users lowered the vote
share of Republican presidential candidate Donald Trump by 0.2 percentage points (p.p.) in
1
See, for example, The New Yorker (2016); New York Times (2017); Allcott and Gentzkow (2017); The
Guardian (2018); UK Parliament (2019).
2
Enikolopov et al. (2020) use a similar empirical strategy based on spatial variation in early adopters of
the social media network VK in Russia to study its effects on protests.

1
 both the 2016 and 2020 presidential elections. The implied persuasion rates are 8.6% and
9.4%, respectively. These estimates are smaller than the estimated pro-Republican effect of
Fox News (DellaVigna and Kaplan, 2007; Martin and Yurukoglu, 2017), the pro-Democrat
effect of the Washington Post (Gerber et al., 2009), or the effect of get-out-the-vote canvassing
on turnout (Gerber and Green, 2000), but larger than the effect of an independent anti-Putin
Russian TV channel on vote shares (Enikolopov et al., 2011) or the effect TV rollout on
turnout (Gentzkow, 2006).
For presidential elections before 2016, we find effects that are small and statistically
indistinguishable from zero. The same holds true for House and Senate races, including the
2016 and 2020 elections. Twitter adoption thus lowered Trump’s vote share but did not do so
for Republican candidates in congressional races in the same election. Together with other
“placebo tests,” this pattern bolsters confidence that our estimates are capturing the effect of
Twitter, which contains more content on presidential than congressional candidates.
An earlier draft of this paper, using only data up to the 2018 election, was posted online
on October 2020. When updating it to include November 2020 election results, we made
no revisions to the research design and regression specifications. In other words, the sample
selection, choice of controls, and variable definitions in the regressions we report were all
decided before the 2020 election results became available. Hence, our 2020 results can be
interpreted as a “pre-registered” research design.3
To shed light on the mechanisms behind these results, we estimate Twitter’s effect on
vote choices reported in the Cooperative Congressional Election Survey (CCES), primary
presidential candidates’ approval in the Gallup Daily Tracker, and county-level results in the
2016 and 2020 presidential primaries. Further, we explore data on the partisanship of political
content on Twitter.
These exercises yield three findings. First, the CCES results indicate that Twitter’s
effect is driven by independents and moderates switching their votes towards the Democratic
candidate (Hillary Clinton). This is consistent with Bayesian updating, since moderates
presumably have weaker priors and are thus more likely to be persuaded.
Second, we find that Twitter also lowered Trump’s vote share during the 2016 primaries,
a finding we confirm using individual-level Gallup candidate approval ratings. We find that
Twitter decreased Trump’s approval ratings and increased Clinton’s with only small effects
on relatively more moderate Republican candidates.4
3
The “pre-registration” document is the October 2020 draft, which is available and “time-stamped” at
Social Science Research Network (SSRN):
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3719998.
4
We also estimate effects for the 2016 and 2020 Democratic primaries, detecting a (positive) effect for
Bernie Sanders in 2020.

2
 Third, we document that political content on Twitter has a pro-Democratic slant. We
classify the slant of tweets based on two complementary approaches: one based on the network
users follow and one using the text of tweets in a machine learning approach in the spirit
of Gentzkow and Shapiro (2010). We apply these methods to the over 460 million tweets
mentioning the presidential candidates in the 2012, 2016, and 2020 elections. We find that
the number and attention (proxied by “likes”) of tweets mentioning Trump was substantially
larger than that of those mentioning Clinton and Joe Biden. Moreover, tweets about Trump
in 2016 and 2020 70% more likely to have Democratic rather than Republican slant. Overall,
our results are consistent with Twitter and its relatively pro-Democratic content persuading
voters with moderate views to not vote for Trump without inducing a more general negative
effect on Republicans.5
Our work contributes to the literature on the impact of media on political outcomes.
Expansions of traditional media such as newspapers, radio, broadcast television, and cable
news have been associated with changes in voter turnout, polarization, and electoral outcomes.6
While a set of papers studies the effect of overall internet access, the effects of social media
per se received less attention.7
A nascent literature studies the political effects of social media on protest participation
(Howard et al., 2011; Enikolopov et al., 2020; Acemoglu et al., 2017; Fergusson and Molina,
2021), xenophobia (Müller and Schwarz, 2020; Müller and Schwarz, 2019; Bursztyn et al., 2019)
and mental health (Braghieri et al., 2021).8 Additionally, a burgeoning field of experimental
research focuses on social media. Bond et al. (2012) and Jones et al. (2017) provide evidence
that online messages on social networks affect voter turnout. Allcott et al. (2020) and
Mosquera et al. (2020) find that individuals who deactivate Facebook react along many
dimensions, including some measures of political polarization. Levy (2021) studies the effect
of randomly assigning Facebook users subscriptions to conservative or liberal media outlets.
Bail et al. (2018) estimate the effect of paying Twitter users to follow a bot with messages
of the opposing political ideology. Most related to our paper is recent unpublished work by
Rotesi (2019), who finds social media negatively affected the Democratic vote share in the
5
Note also that Trump’s platform and impact on social media content was likely distinct from previous
prominent Republican candidates (Bursztyn et al., 2020).
6
See, for example, Gentzkow (2006); Huber and Arceneaux (2007); DellaVigna and Kaplan (2007); Gerber
et al. (2009, 2011); Gentzkow et al. (2011); Enikolopov et al. (2011); Campante and Hojman (2013); DellaVigna
et al. (2014); Larcinese and Miner (2017); Martin and Yurukoglu (2017); Spenkuch and Toniatti (2018); Chen
and Yang (2019).
7
There is evidence that broadband internet (Falck et al., 2014; Gavazza et al., 2019; Campante et al., 2017;
Lelkes et al., 2017) and mobile internet (Manacorda and Tesei, 2016; Guriev et al., 2020) exert political effects.
8
For reviews, see DellaVigna and Gentzkow (2010), Napoli (2014), Strömberg (2015), Enikolopov and
Petrova (2015), and DellaVigna and Ferrara (2015) and in particular Zhuravskaya et al. (2020) for the case of
social media.

3
2008-2016 presidential elections using variation in Twitter adoption resulting from transfers
of NBA players with Twitter accounts.9
Existing research thus provides an incomplete picture. On one hand, social media has
been painted as a key force behind political change and experimental studies indeed suggest
that social media affects individuals’ self-reported political beliefs. On the other hand, it
remains unclear whether social media can indeed persuade voters and affect elections results
at a larger scale. Our paper sheds light on this question by focusing on how Twitter affects
federal elections in the United States.

2 Background: Social Media and Politics

Most Americans use social media platforms or messaging applications. Data from the Pew
Research Center suggest that the most popular services are YouTube (used by 73% of adults
in the U.S.), followed by Facebook (69%), and Instagram (37%) (Pew Research Center, 2019c).
22% of adults in the U.S. use Twitter, a rate similar to that of Snapchat (24%) and Whatsapp
(20%) users. On average, adult users spend more than an hour a day using social networks
(eMarketer, 2019).10
One popular perspective is that online networks, and social media in particular, may
give rise to so-called “filter bubbles” (Pariser, 2011) or “echo chambers” (Sunstein, 2017).
The idea is that social media—unlike traditional mass media outlets—may facilitate the
provision and consumption of one-sided information, either through the use of algorithms or by
allowing individuals to self-select into preferred content. While there is considerable empirical
evidence supporting this idea (e.g. Conover et al., 2011; Weber et al., 2013; Bessi et al., 2015;
Del Vicario et al., 2016; Halberstam and Knight, 2016; Schmidt et al., 2017; Levy, 2021), other
studies have found that individuals are exposed to a wide range of political opinions on social
media (Barberá, 2014; Bakshy et al., 2015; Nelson and Webster, 2017; Beam et al., 2018),
perhaps even more so than via traditional media outlets or personal interactions (Gentzkow
and Shapiro, 2011). Some work also challenges the notion that increased polarization due to
online channels is quantitatively important (Flaxman et al., 2016; Guess, 2018; Boxell et al.,
2019).
9
Our paper differs from Rotesi (2019) not only in its research design, but in that we estimate effects for a
larger set of elections years (including 2020) as well as Congressional elections. Rotesi (2019) only reports
estimates pooling the 2008-2016 presidential elections, differently from our paper. This does not allow to
see if his instrument is uncorrelated with election results before Twitter’s launch and how its possible effects
evolved over time.
10
Pew bases its usage measures on the share of respondents who state they have ever used one of the online
platforms. Twitter reported around 69 million monthly active users in 2019 (see Statista, 2019), which yields
a slightly higher share of around a third of the 210 million adults in the U.S.

4
 Much of the recent public discussion about the role of social media platforms has been
shaped by controversies, including the consulting firm Cambridge Analytica’s involvement
in multiple political campaigns (e.g., The Guardian, 2018); the Russian Internet Research
Agency’s efforts to support Trump’s election campaign (e.g., New York Times, 2017); and the
role of widely shared false information (“fake news”) in both the 2016 U.S. elections (e.g.,
Allcott and Gentzkow, 2017) and the Brexit referendum in the United Kingdom (e.g., UK
Parliament, 2019). Both Hillary Clinton and Donald Trump have argued that these factors
were instrumental in the 2016 election outcome, as has former president Barack Obama
(The New Yorker, 2016). As Brad Parscale, Trump’s digital media director in 2016, put
it: “Facebook and Twitter were the reason we won this thing. Twitter for Mr. Trump.
And Facebook for fundraising” (Wired, 2016). In Appendix Figure A.3, we document that
discussions of social media have become increasingly frequent in major American news outlets.
Mentions of Twitter in particular spiked with the 2016 presidential election when compared
to 2012 levels.
Despite its prominence in the political discourse, the empirical relevance of social media
for electoral outcomes is largely unknown, and some have suggested that concerns about its
effects may be overblown. As one example, in the 2016 presidential election, Trump received
fewer votes from demographic groups with higher propensities to use social media or the
internet more broadly (The Hill, 2016; Boxell et al., 2017, 2018). Indeed, Trump’s broadest
support came from older white voters without college education in rural areas, who are among
the least likely people to use social media actively (Hargittai, 2015; Pew Research Center,
2015, 2018). These patterns seem difficult to square with the idea that online channels were
an important driver of the 2016 presidential election result, although such observations also
do not rule this out.
Further, most social media users—particularly on Twitter—appear to be disproportion-
ately left-leaning. This is not suprising given that most Twitter users are relatively younger,
educated, and urban. While there appears to be a cluster of right-wing networks, Pew
Research Center (2019d) estimates that, in 2018, 60% of Twitter users identified as Democrat
and only 35% as Republican. Among Democrats, those on Twitter are considerably more
liberal and focus less on finding common ground with Republicans (Pew Research Center,
2020). In 2019, 26% of American Twitter users followed Obama and 19% followed Trump (Pew
Research Center, 2019a). Survey evidence suggests that 80% of Twitter content is produced by
people who strongly disapprove of Trump (Pew Research Center, 2019b). “Liberals” are also
more likely to get political news on Twitter or Facebook and follow more media and political
accounts compared to “conservatives” (Pew Research Center, 2014; Eady et al., 2019). Twitter
and Reddit, which are often said to be pro-Trump factors, were considerably more popular

5
among Clinton supporters before the 2016 election (Hargittai, 2015). Although social media
allows users to partially select which content they see, Twitter content disproportionately
leans toward the Democratic party.
We provide additional evidence for the composition of political content on Twitter by
analyzing the Twitter reach of Democratic and Republican politicians. We collected data on
the Twitter accounts of all Senators and House Representatives from the 110th to the 115th
Congresses (2007-2019). In Figure 1, we plot the average number of tweets and followers that
members of each party have on Twitter, as well as the average number of retweets and “likes”
their tweets receive. The patterns here again clearly indicate that Democratic politicians are
more active on Twitter and have larger follower bases than their Republican counterparts.
Tweets by Democrats also receive, on average, three times the number of “likes.”11

3 Data
The main analysis is based on a county-level dataset on election outcomes, political opinions,
and Twitter use. It covers 3,065 counties in 48 states (we exclude Alaska and Hawaii) and
the District of Columbia (except in congressional elections). County-level election results
are from Dave Leip’s Atlas of U.S. Presidential Elections and the MIT Election Lab. We
complement our analysis with individual-level survey data on approval ratings from the
Gallup Daily Tracker and voting data from the Cooperative Congressional Election Study
(CCES). Our measure of Twitter usage is derived from an archive of 475 million geo-located
tweets compiled by Kinder-Kurlanda et al. (2017). We combine this with newly collected
data on Twitter’s early adopters at the 2007 SXSW festival; data on the Twitter activity of
U.S. Congress members; and a large corpus of tweets related to the 2012, 2016, and 2020
presidential elections. Additional county characteristics were obtained from the U.S. Census,
the U.S. Religious Census, the American Community Survey (ACS), and the Bureau of Labor
Statistics (BLS). We describe the individual data sources in more detail below. Appendix
Table A.2 provides additional details and summary statistics.

Election Outcomes. We use county-level data on presidential election outcomes between

1924 and 2020 from Dave Leip’s Atlas of U.S. Presidential Elections. From the same source,
we also obtained county-level voting data for the Republican and Democratic primaries in
2016 and 2020. We complement this with county-level results on Senate and House elections
from the MIT Election Lab for the 1996-2020 period. In all cases, we focus on two-party
11
In Appendix Figure A.2, we confirm that these patterns are not driven by a small group of Congress
members by showing that they also hold when we compare the median Twitter reach of Democrats and
Republicans.

6
vote shares. Figure 2 visualizes the Republican party’s vote share in the 2016 presidential
elections.12

Individual-Level Voting Decisions. The Cooperative Congressional Election Study

(CCES) is a nationwide survey that collects information on voter behavior in two waves (before
and after the election). We focus on votes for Trump and Clinton in 2016 and 2020. The
CCES contains a rich set of individual characteristics, including political affiliation, family
income (in 12 bins), gender, race, education (in 6 bins), marital status, age, and interest in
the news. Table A.3 provides summary statistics (weighted by sample weights). The CCES
also uses administrative data on turnout records to verify its respondents have voted.

Presidential Candidate Approval. The Gallup Daily Tracker provides individual-level

survey data for a sample of 1,000 individuals per day since 2009.13 During the 2016 presidential
campaign, it fielded survey items regarding approval of Republican and Democratic presidential
candidates. This allows us to investigate Trump’s pre-election approval relative to other
candidates (e.g. Clinton or Ted Cruz). The data also include a rich set of individual
characteristics, including political affiliation, county of residence, income (in 10 bins), gender,
race, marital status, age, and education (in 6 bins). Table A.4 in the Appendix provides
summary statistics.14

Twitter Usage. We construct a measure of county-level Twitter usage based on a sample

of 475 million geo-coded tweets collected by Kinder-Kurlanda et al. (2017).15 The tweets were
collected between 2014 and 2015 using the Twitter Streaming API by selecting a geographic
bounding box around the mainland US. The Streaming API continuously returns all geo-
located tweets within the bounding box as long as the sample does not exceed 1% of all tweets.
Information on a tweet’s geo-location either come from the GPS coordinates of a mobile phone
or from a WiFi/IP address in case a computer is used. Both types of information allow for a
precise assignment to a US county. At the start of the data collection by Kinder-Kurlanda
et al. (2017) in 2014, Twitter had not yet introduced the feature that allowed users to tag
specific places in their tweets. This avoids collecting geo-located tweets based on arbitrary
12
While senatorial and presidential elections are decided at the state level and House elections at the
congressional district level, counties are usually smaller geographic units and far more numerous. Additionally,
unlike congressional districts, county boundaries are fixed over our sample period, allowing us to observe
changes across years.
13
The Gallup Daily Tracker for the 2020 election is not available at the time of writing.
14
For some auxiliary estimations in the Online Appendix, we also collapse responses about presidential
approval of Trump on the county-level using weighted averages based on the number of survey respondents in
each county.
15
These data are available in the Gesis Datorium at https://datorium.gesis.org/xmlui/handle/10.7802/1166.

7
decisions by users (e.g. tagging their holiday location) and prevents users from tagging wrong
locations (either intentionally or by accident).
The individual tweets from this dataset are already assigned to counties. Additionally,
we collected the underlying user profiles for each tweet in the database. This allows us to
construct a user-based measure by assigning users to the county from which they tweet most
frequently. The resulting measure, which we use throughout the paper, is a proxy for the
number of Twitter users per county, based on 3.7 million individual users (around 7% of the
Twitter population in 2015). Figure 3a plots the number of Twitter users per capita across
counties. Each user profile further provides us with a short biography and the date that each
user joined Twitter. We use the join dates to construct a time-varying proxy of Twitter usage
based on how many of the Twitter users had opened an account at each point in time.
The great advantage of this dataset is that it allows us to provide individual-level
evidence for the adoption of Twitter in a county following the SXSW festival and further
compare user profiles in different counties. The drawback of using geo-located Twitter data is
that only a sub-sample of tweets is geo-located. To overcome concerns of measurement error
in our Twitter measure, we validate the data in two ways. First, Appendix Figure A.1a shows
that our Twitter usage measure’s evolution closely tracks the number of daily Twitter users
from Statista (2019), which were directly obtained from the platform. Secondly, our measure
of county-level Twitter usage also strongly correlates with the number of Twitter users in a
county based on the GfK Media Survey (see Figure A.1b). Lastly, note that measurement
error is less of a concern in our setting because we largely rely on 2SLS estimation throughout
the paper. We return to the discussion of measurement error in Section 5.

Twitter Data for the South by Southwest Festival. We collected data for our instru-
ment for Twitter usage, based on early adoption during the SXSW festival, through the
Twitter API. More specifically, we scraped the account data for 658,240 users who followed
the Twitter account of SXSW Conference & Festivals (@SXSW) at the time of collection
(January 2019). We assign these users to counties based on the location people report in their
user profile.16
A user profile contains the month and year that they joined Twitter, which allows us to
determine the number of SXSW followers in each county that joined Twitter in a particular
month. The two key variables in our analysis are: i) the number of SXSW followers that
joined Twitter in the month of March 2007 and ii) the number of SXSW followers that joined
Twitter during 2006 (the year the platform was launched). We refer to (ii) as the number of
SXSW followers who joined before the March 2007 festival. We also scraped the follower lists
16
Of the 44,625 SXSW followers who joined between 2006 and 2008, we are able to geo-code 25,830 (58%).

8
of SXSW followers who joined in March 2007, which allows us to investigate the connections
of Twitter users to the SXSW festival. Further, we additionally collected tweets mentioning
the festival, based on the term “SXSW,” as well as a proxy for overall Twitter activity based
on the 100 common English words.17 We use these measures to document the SXSW festival’s
impact on local Twitter adoption.18

Data on Political Twitter Activity. We scraped the tweets and user profiles and followers
of the 901 Senators and House Representatives from the 110th to 115th (2007-2019) Congress
who have Twitter accounts. This includes 424 Democrats and 465 Republicans.19 In total,
the data contain 4,300,579 tweets, which we use to analyze the Twitter reach of Democratic
and Republican Congress members. Appendix Table A.1 lists the 20 Congress members with
the most Twitter followers.
We complement this dataset with election-related tweets to shed light on the overall
partisan slant of Twitter activity during the 2012, 2016, and 2020 elections. For each election,
we obtained the universe of tweets mentioning the last name of a Democrat and Republican
presidential candidates.20
To determine the likely political affiliation of Twitter users, we create two measures of
political slant. The first measure is based on the political accounts a user is following. In
particular, we check whether a user follows more Democrat or Republican Congress members
on Twitter. If they follow more Republican than Democrats, all their tweets would be
classified as Republican. In case a user either does not follow any Congress members or an
equal number of Congress members from either party, their tweets are classified as neutral.21
The second measure of political slant is based on the similarity of the text of tweets
to those sent by Republican or Democratic Congress members. We train a L2 regularized
logistic regression model separately for each election based on 901 Congress members’ Twitter
accounts to classify whether a tweet contains language frequently used by either Republican or
17
We report the full list of words in Table A.7.
18
Data on SXSW 2007 attendants (e.g., their county of residence) is not available, despite our efforts to
obtain it from the SXSW organizers on multiple occasions.
19
The remaining 12 politicians are either Independents or switched their party affiliation.
20
For the 2012 election, we use data collected by Diaz et al. (2016), comprising 24 million tweets containing
either “Obama” or “Romney” for the period from July 1, 2012 through November 7, 2012. For 2016, we use
the archive from Littman et al. (2016), which contains 280 million tweets, collected between July 13, 2016, and
November 10, 2016. The 2020 election tweets are based on the archive from Chen et al. (2020), which covers
the period from March 2020 to November 2020. To make these datasets comparable, we restrict the 2016
election sample to tweets mentioning either “Clinton” or “Trump” (112 million tweets). Similarly, we restrict
the 2020 data set to the time period from July 1, 2020 through November 3, 2020 and tweets mentioning
either “Biden” or “Trump” (339 million tweets).
21
The idea of using the Twitter network to determine a user’s ideology is inspired by Barberá (2015).

9
Democratic politicians.22 We then use this classifier to predict a partisan score between 0 and
1 for each of our election-related tweets. These scores can be interpreted as the probability of
a tweet with the same content being sent by a Republican. As such, our approach is similar
to how Gentzkow and Shapiro (2010) measure newspaper slant. Both approaches lead to
similar overall slant classifications for the elections tweets in our data.

Additional County Characteristics We collect county-level demographic control vari-

ables from the U.S. Census and the ACS. In particular, we use information on population,
population share by age group and ethnicity, poverty rates, and education levels. We also
obtained industry-level employment shares and unemployment rates from the BLS. Additional
controls on county media usage patterns are from Simply Analytics. We also construct
geographical controls such as the distance from Austin, TX, where SXSW takes place every
year; population density; and county size (in square miles). For one set of results we also use
donation data from OpenSecrets. Appendix Table A.6 provides a description of the variables.

4 The 2007 South by Southwest Festival and Early

Twitter Adoption
The empirical strategy behind our main results exploits a shock to early-stage Twitter adoption
connected to the 2007 SXSW festival, as in Müller and Schwarz (2019). This section discusses
the key role of the festival in boosting the platform’s popularity and documents how it created
a persistent effect on its spatial diffusion.23
Founded in March 2006, Twitter was largely unknown before SXSW 2007. Twitter’s
popularity increased dramatically after the festival, where Twitter strategically placed screens
in the conference hallways and allowed users to sign-up by simply sending a text message
to a predefined number. As a result, speakers and bloggers in attendance broadcasted the
platform to the outside world, and Twitter went on to win the South by Southwest Interactive
Web Award Prize.
The importance of SXSW 2007 has also been stressed by the platform’s founders. As
co-founder Evan Williams explained in a post on Quora (Quora, 2011):
22
We clean the text of the tweets by removing common words (stopwords) and by reducing the words
in each tweets to their morphological roots (lemmatizing). The input is based on unigrams, bigrams, and
trigrams from these tweets. We choose the optimal normalization strength using 10-fold cross-validation. The
resulting classifier achieves high out-of-sample F1-scores, e.g. 0.904 for the tweets during the 2020 presidential
election. We provide additional details regarding the machine learning classifier in Online Appendix A.1.,
which also visualizes the most predictive terms identified by the classifiers.
23
SXSW is an annual conglomeration of parallel film, interactive media, and music festivals and conferences
organized jointly that take place in March in Austin, TX.

10
 “We didn’t actually launch Twitter at SXSW – SXSW just chose to blow it up.
We launched it nine months before – to a whimper. By the time SXSW 2007 rolled
around, we were starting to grow finally and it seemed like all of our users (which
were probably in the thousands) were going to Austin that year ... I don’t know
what was the most important factor, but networks are all about critical mass,
so doubling down on the momentum seemed like a good idea. And something
clicked.”24

SXSW’s immediate impact on Twitter’s popularity in early 2007 can be seen in Figure 4a,
which plots our proxy for the daily number of tweets as well as the number of tweets explicitly
mentioning SXSW. The figure shows that Twitter’s growth rate accelerated during the festival,
visible as the spike in SXSW-related tweets. The month-to-month growth rate of Twitter
quadrupled with the start of the SXSW festival.25 After SXSW 2007, Twitter experienced
further rapid growth (Venture Beat, 2008). The platform went from an average of 5,000
tweets a day in 2007 to 300,000 in 2008, and 2.5 million in 2009 (Twitter, 2010). In 2019,
users sent roughly 500 million tweets a day.
We exploit that the SXSW festival had persistent effects on Twitter’s spatial diffusion.
This is likely the result of network effects that are key to the social media experience, as
a larger number of users makes it more interesting for potential new users to join. Such a
mechanism also applies at the local level. For example, a boost in the number of neighbors,
personal connections, local businesses and/or people who play a prominent role in an area
should also boost the value of joining the platform for those living there. As Evan Williams’
quote above notes, “networks are all about critical mass,” and initial differences in adoption
can lead to persistent differences in network adoption. Enikolopov et al. (2020) document a
similar mechanism for spatial dispersion in adoption of the Russian VK social network: the
home towns of the first university students invited to be users in 2006 exhibit substantially
higher number of users in 2011.
We provide further support for this hypothesis by investigating whether the inflow
of early-stage adopters put these counties on a differential growth path of Twitter usage.
Figure 4b plots the estimates of βτ from the following panel event study regression at the
county (c) and week (t) level:
X X
tweetsct = βτ SXSWcM arch2007 × 1(t = τ ) + δτ SXSWcP re × 1(t = τ ) + θc + γt + εct .
τ τ

24
Appendix Figure B.1 provides Williams’ full post describing the role of SXSW 2007.
25
Our proxy for Twitter usage is created by scraping tweets that contain any of the 100 most common
English words listed in Table A.7. Our data contain any tweet that contains at least one of these words. We
should therefore obtain a large fraction of the English speaking tweets at that point in time.

11
where tweetsct is the log of (one plus) the number of tweets in county c on week t, SXSWcM arch2007
is the logarithm of (one plus) the number of SXSW followers in county c that joined Twitter
on March 2007 and SXSWcP re is a similarly defined variable for followers that joined Twitter
before March 2007. βτ thus illustrates, conditional on county and week fixed effects, the
difference in the number of tweets sent from counties with relatively larger numbers of SXSW
followers that joined on March 2007. The variables are standardized to have a mean of zero
and standard deviation of one. The whiskers represent 95% confidence intervals based on
standard errors clustered at the state level. The sample includes the period between the third
and fourteenth week of 2007.
Figure 4b illustrates that home counties of SXSW followers who joined during the
festival in March 2007 saw a rapid, disproportionate increase in Twitter usage around the
time of SXSW. Importantly, however, this increase came only after the SXSW festival, and
we find no evidence for pre-existing trends. In Appendix Figure B.2 we show further that, in
line with our expectations, March 2007 is also a clear outlier when it comes to the number of
people who started following the SXSW festival. This is consistent with the idea that SXSW
was a catalyst for the spread of Twitter in the United States.
Appendix Figure B.3a presents additional evidence on the long-term adoption effect of
the 2007 SXSW festival. It plots estimates from a similar regression as the one in Figure 4b
but in a county-quarter panel covering the period from Twitter’s launch in 2006 to 2016.
The dependent variable is substituted by the number of Twitter users per capita in a county
based on our baseline measure. The resulting S-shaped pattern in the figure is consistent with
models of technology adoption in the presence of network effects. More importantly, we find
that the amount of early adopters in a county still matters for the amount of Twitter usage
today.26

5 Empirical Framework
Our identification strategy leverages the 2007 SXSW festival as a shock to early Twitter
adoption. We show that, conditional on a set of controls (described in further detail below),
a county’s number of SXSW followers that joined Twitter in March 2007 is uncorrelated with
levels and trends in election outcomes before Twitter’s launch and during its early years. It is
26
Additionally, Figure B.3b shows just how dominant Twitter users connected to the SXSW festival were
among early adopters. In 2007, we estimate that around 60% of Twitter users either followed the SXSW
festival or followed someone who followed SXSW and joined in March 2007. As the number of Twitter users
increased over time, the importance of SXSW followers in the platform declined. But as Figure B.3a shows,
the festival created persistent differences at the county level. The next section outlines how we use the SXSW
festival in our 2SLS estimates.

12
also uncorrelated with a host of observable county characteristics. This feature of the data can
be interpreted as idiosyncratic factors (e.g., who attended the 2007 SXSW, who decided to
join Twitter at the time), giving us a “natural experiment” or “exogenous shock” in Twitter
adoption that allows to estimate its effect on election outcomes. This interpretation is, of
course, not self-evident, and we provide several pieces of evidence to support it.
An important concern is that counties whose population are more interested in the
SXSW festival (and its Twitter account) may be systematically different from other counties.
To address this issue, our empirical strategy exploits variation in the exact timing of when
Twitter users interested in SXSW joined the platform across counties. In particular, our
regressions control for the number of SXSW followers who joined in the months before the
festival. Intuitively, our empirical strategy compares a “treatment” group of counties with
SXSW followers that joined in March 2007 (during the festival) against a “control” group of
counties with followers that joined before. While both groups of followers were interested in
SXSW, we show that only the number of followers that joined on March 2007 are predictive of
later Twitter adoption, consistent with the evidence that users that joined during the festival
were key in the platform’s diffusion. In contrast, counties with more users that joined before
the festival do not have more additional Twitter users in subsequent years.27
The “treatment” and “control” counties are similar along several characteristics. Ta-
ble A.5 compares the average characteristics of three types of counties relevant for our
identification strategy: 1) the 47 counties with SXSW followers that joined Twitter both in
March 2007 and the “pre-period;” 2) the 108 counties with SXSW followers that joined in
March 2007 (but none in the “pre-period”’); and 3) the 20 counties with SXSW that joined in
the “pre-period” (but none in March 2007). Differences in vote shares in the 1996 presidential
election, demographics (e.g., race, age, education), and media consumption (e.g., share that
watches Fox News) are quantitatively small or zero. This is particularly true for groups
(2) and (3) — which are key to the identification — with t-tests indicating that differences
between the two groups are not statistically different from zero.28 The geographical variation
in the three groups of counties is shown in Figure 3b. As the results in Table A.5 suggest, the
counties do not differ systematically in size and how distant they are from major American
cities.
Moreover, observable individual characteristics of SXSW followers who joined Twitter
in March 2007 and the “pre-period” are also similar. We validate this using data on Twitter
27
An alternative approach is to compare the counties of users who signed up for Twitter during SXSW
2007 with those of users who signed up during other festivals in the same year. We discuss the results from
such an exercise in the robustness section below.
28
Given the large number of county characteristics, we report Šidàk-corrected t-statistics, which are smaller
than those generated by applying the Bonferroni correction.

13
user profiles we obtained from the platform. Table B.1 shows that followers who joined in
March 2007 have similar first names and profile descriptions compared to those that joined
before: users in both groups tend to have common names (e.g., “Michael” or “Chris”) and use
words such as “founder” or “tech” to describe themselves in their profiles. The correlations of
the frequency of first names and terms used in their bios between the two groups are 0.63 and
0.89, respectively. We also investigate differences in the political leanings of the two groups
using the network-based methods we outline in Section 3. In particular, we test whether the
users in March 2007 follow more Democrats or Republicans than the users in the “pre-period”.
We find that the political leanings of the two groups are nearly identical. A t-test rejects
differences in the average political slant with a p-value of 0.93.

Specification. Motivated by the evidence above, our main results are based on estimating
the following two equations:

T witter usersc = α + β · SXSWcM arch2007 + γ · SXSWcP re + Xc δ + ξc (1)

yc = α0 + β 0 · SXSWcM arch2007 + γ 0 · SXSWcP re + Xc δ 0 + ζc , (2)

where c indexes counties, SXSWcM arch2007 is the logarithm of (one plus) the number of
SXSW followers in county c that joined Twitter on March 2007, and SXSWcP re is a similarly
defined variable for followers who joined Twitter before March 2007. Xc is a vector of control
variables that hold constant geographical factors (e.g., population density, distance from
Austin, TX), demographic factors (e.g., the share of the population in different age and
ethnic groups), socioeconomic factors (e.g., the share of adults with a high school degree
or employed in IT), factors related to the “China shock” (e.g., exposure to Chinese import
competition), and previous election results. Note that the right-hand side of both equations is
similar. T witter usersc is the logarithm of the number of Twitter users in the county (during
2014-2015). yc are election outcomes (e.g., vote shares), which we estimate in both levels and
changes (e.g., yc can be the vote share in 2016 or the change in vote shares between 2000 and
2016).
In a 2SLS framework, equations (1) and (2) are the first-stage and reduced form, while
the second stage is

yc = φ + θ · T witter
\ usersc + π · SXSWcP re + Xc ρ + εc , (3)

14
where T witter
\ usersc is predicted from the first stage regression in equation (1). We weigh
observations by turnout (total number of votes cast) in the 2000 presidential election.29 We
cluster standard errors at the state level.30

Identification. Formally, the identification condition for the effect of Twitter users (θ)
is that E(SXSWcM arch2007 · εc ) = 0 holds. Intuitively, this states that, conditional on the
SXSWcP re and other controls (Xc ), the number of SXSW followers who joined in March 2007
is uncorrelated with other determinants of political outcomes yc , implying that it only affects
political outcomes via Twitter usage (the “exclusion restriction”).
We provide six pieces of evidence in support of this condition. First, as discussed above,
including the SXSWcP re control implies that the identifying variation comes from comparing
counties with similar observable characteristics.
Second, the coefficient of SXSWcP re is small and statistically insignificant in our first
stage regressions. This provides us with a “placebo” test based on checking if it is also
unrelated to political outcomes in the reduced form and 2SLS regressions. Intuitively, we
have two variables that are correlated with interest in the SXSW festival among early Twitter
adopters, but only one predicts Twitter users in later years, allowing us to disentangle interest
in the festival from its effect via more Twitter users.
Third, we provide additional placebo tests for other festivals in 2007 and show that
none of these other festivals is either correlated with higher Twitter adoption or changes in
the Republican vote share today. This allows us to rule out that our effects are simply driven
by the selection of users into attending festivals that are highly similar to SXSW.
Fourth, estimating equations (2) and (3) for different time periods shows that SXSWcM arch2007
does not correlate with both levels and trends in election outcomes before Twitter’s launch in
2006 and in its early years, when the platform had few users and was unlikely to affect election
outcomes. Intuitively, outcomes in “treatment” and “control” counties behaved similarly
before Twitter could plausibly affect elections.
Fifth, we find an effect of SXSWcM arch2007 on Trump’s vote share in 2016 and 2020 but
not on House and Senate elections (neither in 2016 nor 2020 or other periods between 2000
and 2018). This pattern is consistent with an effect of Twitter, since there is more content on
presidential candidates than on congressional elections in the platform.
29
We weigh observations to make our sample representative of national election results. For example, due
to many less populated counties tilting Republican, the unweighted average Republican vote share across
counties in the 2016 elections is 64%. Weighing makes our sample mean match the national average of 46%.
Moreover, we obtain similar results without using weights or using election-year turnout as weights (Appendix
Table C.3), suggesting that effect heterogeneity along dimensions correlated with weights do not play an
important role in our context (Solon et al., 2015).
30
We consider spatial standard errors using the methods described in Colella et al. (2019) for robustness.

15
 Sixth, results based on survey data suggest the effects are concentrated among moderate
or independent voters, which is also the expected pattern from Twitter having a causal effect
due to voter persuasion.
Stated differently, a violation of the identification condition would require an omitted
variable that correlates with SXSWcM arch2007 , T witter usersc , and yc but is uncorrelated
with: i) SXSWcP re as well as followers of other festivals, ii) levels and trends in election
results before Twitter’s launch and rise to popularity, iii) the observable variables presented in
Table A.5, and iv) election results in congressional elections both during the Trump elections
and before, while also v) being correlated with vote choices of moderate voters. Our argument
is that the existence of such an omitted variable is implausible to an extent that allows us to
interpret θ as the effect of Twitter users on election outcomes.
Measurement error in county-level Twitter usage and SXSWcM arch2007 is also unlikely to
explain an effect in 2016 and 2020 presidential elections, but no effect in previous presidential
elections or congressional elections. Moreover, SXSWcP re and the measures for other festivals
are constructed similarly as SXSWcM arch2007 and should thus have similar measurement error.
However, SXSWcP re and the other festivals are uncorrelated with Twitter usage and election
outcomes.
Lastly, another possible concern is that the SXSW adoption shock led to differences
in the composition of Twitter users when compared to other U.S. counties. In particular,
one might be concerned that the SXSW festival lead to a more liberal Twitter population
in the treated counties. While this would not influence the causal interpretation of our
findings, it could make the local average treatment effect harder to interpret. Three pieces of
evidence suggest that this appears to be an unlikely concern. First, as we show in Appendix
Figure B.3b, Twitter’s user base became less connected to the SXSW festival over time and,
in this process, likely reached people from more diverse backgrounds. Second, the findings of
Müller and Schwarz (2019) indicate that the SXSW adoption shock was associated with an
increase in hate crime with Trump’s presidential run. This suggests that the shock eventually
reached even the right-wing fringes of the political spectrum. Third, we can directly address
this concern by comparing the profiles of Twitter users in SXSW home counties with those
in the rest of the country. The results are presented in Appendix Table B.2. We find that
the user profiles in SXSW counties are highly similar to the general Twitter population. If
the Twitter population in SXSW counties was significantly more liberal, their Twitter and
names and biographies should also be different, as Oliver et al. (2016) document that names
predict political ideology. We find similar results when we look at which politicians users in
the different counties follow. If anything, Twitter users in the “pre-period” counties appear
to have a slightly more liberal Twitter network.

16
 To be transparent we want to stress what our findings do not imply. First, they cannot
speak about social media platforms other than Twitter, such as Facebook. Our empirical
strategy exploits a “shock” specific to early Twitter adoption and we do not have a credible
research design to estimate the effects of other platforms. While many other platforms share
similarities with Twitter, such as being popular among younger and more educated people
in urban areas (Pew Research Center, 2019c), other platforms may have different effects
on political outcomes. Second, our research design cannot separate the effect of particular
types of social media content on Twitter (e.g foreign governments or misinformation), but
rather speaks to the overall effect of Twitter exposure. Third, like other papers in media
economics, we estimate a “partial equilibrium” effect. In our case, we estimate the effect of
adding Twitter users to a county while keeping other counties’ Twitter use constant. We
thus cannot address whether Twitter had a national-level effect on the election (e.g., Trump’s
tweets driving traditional media content).

6 Results
6.1 Main results
First-stage. Table 1 reports results from estimating equation (1) with different sets of
control variables (described in Appendix Table A.6). The results indicate that counties with
more SXSW followers who joined Twitter in March 2007 have higher numbers of Twitter
users during 2014-2015. Since the variables are in logs, the coefficients can be interpreted
as elasticities. A 10% increase in SXSW followers in March 2007 is associated with 5.2%
more Twitter users. The results do not seem to be sensitive to the set of included covariates.
For example, the distance from Austin, Texas (the location of SXSW) has no significant
explanatory power. Importantly, the coefficients on SXSW followers before the 2007 festival
are statistically insignificant and small in size: Twitter usage in 2014-2015 is not higher in
areas with more SXSW followers who joined Twitter before March 2007.
Figure 5 presents the graphical representation of the estimates in column (5) of Table 1.
Specifically, we show a binned scatter plot of T witter usersc against SXSWcM arch2007 after
both variables are “residualized” by partialling out the control variables. The figure is
constructed by dividing the x-axis variable into 40 equal-sized bins and plotting the average
values of both variables in each bin.31
31
The fitted line is based on the unbinned data. Observations are weighted by turnout in the 2000 presidential
election. This procedure guarantees the slope of the fitted line matches the estimate on column (5) of Table 1.

17
 Reduced Form and 2SLS Estimates. Table 2 shows the reduced form estimates from
equation (2) and both OLS and 2SLS estimates of equation (3), focusing on the Republican
vote share in the 2016 and 2020 presidential elections. The specifications across columns
match those in Table 1. Panel B indicates that the number of SXSW followers who joined
Twitter in March 2007 is associated with a lower Republican vote share. Panel C presents
the 2SLS effects of Twitter usage on vote shares. The 2SLS estimate in column (5) indicates
that a 10% increase in the number of Twitter users in a county lowers Trump’s vote share
by 0.21 p.p. (e.g., a reduction from a 46.1% vote share to 45.8%).32 We discuss potential
differences between LATE and ATE as well as heterogeneous treatment effects at the end of
this section. The results for the 2020 presidential election shown in Table 2 column (6)-(10)
are nearly identical.
Figure 6 plots the reduced form and OLS estimates from Table 2 graphically, specifically
the models in columns (5) and (10). These figures are constructed similarly to Figure 5 but
show the Republican vote share on the y-axis. The estimated slopes are negative.
A first draft of this paper, using only data up to the 2016 election, was posted online on
October 2020. When updating it to include the November 2020 election results, we made no
revisions to the research design and regression specifications (e.g., sample selection, choice of
controls, and variable definitions). In the case of Table 2, columns (1)-(5) are exactly the same
in this version and in the October 2020 version. Since columns (6)-(10) replicate the same
specifications (“right hand sides”) using 2020 election outcomes, this can be interpreted as a
“pre-registered” research design. We followed the same approach throughout this draft: every
figure or table presenting results for the 2020 election exactly replicates the specifications
used for previous elections in our October 2020 draft.33
Although the estimated effects are always negative and significant at the 1% level,
comparing columns shows that the effect sizes vary somewhat with the inclusion of controls,
especially demographic and socioeconomic ones. This sensitivity to controls is perhaps
expected given their explanatory power over vote shares. The results in other papers that
explore effects on vote shares in similar frameworks, such as DellaVigna and Kaplan (2007),
Martin and Yurukoglu (2017), and Autor et al. (2020), show a similar sensitivity to controls.
To further probe the sensitivity of our results to different controls, we provide three
separate pieces of evidence. First, we apply the Oster (2019) approach of using coefficient
stability to gauge the potential importance of unobservable variables in driving the results.
We compare our reduced form (Panel B) specifications with all controls (columns 5 and 10) to
32
The F-statistic of our estimated first-stage range from 70 to 120. This suggests that estimation and
inference concerns related to weak instruments are unlikely to apply in our case.
33
The October 2020 draft is available at Social Science Research Network (SSRN):
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3719998.

18
those with the fewest controls (columns 1 and 6) and obtain an “Oster-δ” of approximately 7
for both 2016 and 2020. This suggests, in order for the true effect to be zero, unobservable
variables would have to be seven times as “important” as the (numerous) controls added in
columns (5) and (10) in driving selection into “treatment.”34
Second, Appendix Table C.1 provides further evidence for the robustness of our findings
to the inclusion of different controls. It allows for more flexible interactions between observ-
able characteristics by using partialling-out LASSO variable selection (Chernozhukov et al.,
2015a,b).35 More specifically, we estimate regressions in which we allow for state fixed effects
and all possible interactions of the control variables (i.e., up to 654 controls). The LASSO
then selects controls that either predict the vote shares, SXSW participation, or Twitter usage.
All estimates are statistically significant and of similar magnitude as our baseline estimates.
Third, Figure D.3 plots the estimated θ of our 2SLS equation (3) while flexibly allowing
the included control variables to vary. The resulting “specification curves” suggest that our
results (for both the 2016 and 2020 elections) are robust to how our regressions are specified.
The estimated coefficients are always negative, almost always statistically significant at the
5% level, and in the overwhelming number of specifications considerably more negative than
our “baseline estimates,” which is marked by the vertical line.

Magnitudes and Persuasion Rates. To interpret the magnitudes of our estimates, we

calculate a persuasion rate following DellaVigna and Gentzkow (2010). It can be approximated
t
as θ · e(1−y) , where θ is the 2SLS estimate, y is average Republican vote share, e is the average
exposure of American adults to Twitter, and t is the share of adults that turn out to vote.
Using the estimate for θ from columns (5) and (10) of Table 2, the persuasion rate is 8.6%
and 9.4% for 2016 and 2020, respectively. It implies that, in 2016, one out of every twelve
active Twitter users that voted for Clinton would not have done so if they had not been
exposed to the platform.36
34
The R2 of regressions with the fewest controls (columns 1 and 6) is approximately 0.6, while it is 0.94
for the regressions with the most controls (columns 5 and 10). Intuitively, the observable controls explain a
large part of the variation (the R2 of 0.94) in vote shares but only generating a relatively modest change in
coefficient sizes. This is what generates a large “Oster-δ.”
35
Note that, unlike most other regressions reported in the paper, these regressions are unweighted
36
The persuasion rate re-scales effect sizes by how many individuals are exposed to the platform and how
many are not already persuaded. For marginal changes in exposure, the formula is f = dy t
de · 1−y (DellaVigna
and Gentzkow, 2010). Since our estimate θ is the semi-elasticity dy t
de · e, we obtain f = θ · e(1−y) . In 2016,
y = 0.46, t = 0.55, while y = 0.47 and t = 0.62 in 2020. We assume e = 0.25 for both periods, based on
roughly a quarter of American adults being active Twitter users (Pew Research Center, 2019c). This implicitly
assumes that Twitter usage among voters is the same as the overall population. If voters are over-represented
among Twitter users, the persuasion rate would be smaller. On one hand, Twitter users are younger (which is
associated with lower turnout) but more educated (which is associated with higher turnout) than the general

19
 This rate is smaller than the estimated pro-Republican persuasion rate of Fox News,
which Martin and Yurukoglu (2017) estimate to range between 27% and 58% (depending
on the year).37 It is also smaller than the 19.5% pro-Democrat persuasion rate that Gerber
et al. (2009) estimate for the Washington Post. As a further comparison, Gentzkow et al.
(2011) estimate a persuasion rate of 12.9% for the effect of reading a local newspaper in the
1869-1928 period on voter turnout. DellaVigna and Gentzkow (2010) report that randomized
get-out-the-vote canvassing experiments have estimated persuasion rates in the 10-15% range.

Placebo Test: Interest in SXSW Before March 2007. The coefficients on SXSWCP re
in Table 2 are statistically insignificant and substantially smaller than those on SXSWcM arch2007 .
As discussed in Section 5, this provides support for our identification condition (exclusion
restriction). Suppose that our instrument merely captured that counties with an interest
in SXSW’s Twitter account during the platform’s early years also differ in (unobservable)
characteristics that predict the 2016 election outcome. If this was the case, the coefficients
on SXSWCP re should be similar in size to those on SXSWcM arch2007 . Intuitively, we have two
variables that are correlated with interest in the SXSW festival, but only one predicts Twitter
users in later years, allowing us to disentangle interest in the festival (and its correlates) from
its effect via more Twitter users.

Placebo Test: Other Festivals in 2007. We also provide an additional placebo check by
investigating five other festivals that are similar in nature to SXSW (Burning Man, Coachella,
Pitchfork, Austin City Limits, and the Electric Daisy Carnival).38 One of these placebo
festivals, Austin City Limits, takes place in the same city as SXSW. For this exercise, we
construct analogous measures to our instrument for other festivals using the same exact
procedure: the Twitter followers of the respective festival that joined in the month the festival
took place in 2007. In Appendix Table C.2, we then show that none of these festival variables,
including that for Austin City Limits, has predictive power for future Twitter usage. More
importantly, the variables are also essentially uncorrelated with 2016 and 2020 presidential
vote shares. These results provide further support for our identifying assumption and suggest
that a spurious relationship between counties’ Twitter users interest in festivals during 2007
and future election outcomes cannot explain our findings.
population. Note also that we estimate county-level effects, which may also capture local spillovers and social
interaction effects.
37
DellaVigna and Kaplan (2007) estimate a smaller persuasion rate of 11.6% for Fox News.
38
These festivals take place in the Black Rock Desert (NV), Indio (CA), Chicago (IL), Austin (TX), and
Las Vegas (NV), respectively.

20
Further Robustness and Additional Tests. The Online Appendix presents a number
of additional sensitivity checks. In Table C.3, we consider changes to the baseline regression
specification. In particular, we allow for unweighted regressions; weighting by the relevant
election-year turnout (2016 or 2020); alternative functional forms of the pre-SXSW user
variable; restrict the sample to the sub-sample of counties where we observe either SXSW
followers who joined in March 2007 or the pre-period; allow for flexible spatial correlation in
the standard errors; and per capita measures of Twitter usage. Further, Table C.5 replaces
our baseline time-invariant measure of Twitter usage (making use of all available user data)
with a time-varying measure based on how many users were using the platform in a given
year. None of these adjustments make a substantial difference in the magnitudes or statistical
significance of the estimates.
Online Appendix Table D.1 reports results for additional outcome variables, all of which
support the idea that Twitter exerts a pro-Democrat effect. In column (1), we use a probit
IV model to investigate the likelihood of a county switching from Obama in 2008 to Trump
in 2016. The coefficient suggests that a one standard deviation increase in Twitter usage is
associated with a -24% lower probability of a county electing Obama in 2008 and Trump in
2016. Columns 2 and 3 look at changes in campaign donations to Democrats and Republicans
between 2000 and 2016. We find a positive and statistically significant effect for donations to
Democrats, and no effect for Republicans. Lastly, columns 4 and 5 look at approval ratings
for President Trump in 2017 based on data from the Gallup Daily Poll. We find that exposure
to social media is associated with a decrease in Trump’s popularity, and more so among
Republicans.

The 2007 SXSW Shock and Previous Election Outcomes Table 3 repeats the analysis
from column (5) of Table 2 using Republican vote share in the previous presidential elections
of 2000, 2004, 2008, and 2012 as the dependent variable.39 All estimates for those years are
substantially smaller than the ones for 2016 and 2020 and statistically insignificant. For 2000,
2004 and 2008, this can be interpreted as a placebo or pre-trends test: conditional on the
covariates, our instrument is uncorrelated with outcomes before Twitter’s launch (2000 and
2004) and when the platform had few users (2008). In 2012, Twitter already had a substantial
user base, so our estimates can be interpreted as a genuine “zero effect”; we return to a
comparison of the 2020, 2016, and 2012 results in Section 6.3. The estimates for 2000, 2004,
2008, and 2012 are also jointly statistically insignificant (p-value=0.329). The average effect
39
Note that the right-hand side of the regressions remain the same as in the previously reported regressions.
That is, the instrument is the same and the “endogenous variable” in the 2SLS is Twitter users measured in
2014-2015. We explore the role of time-varying Twitter user measures on Appendix Table C.5.

21
 in 2016 and 2020 is statistically different from the average effect in 2000, 2004, 2008, and
2012 (p-value=0.002).
In Appendix Figure C.1, we further show the reduced form estimates for presidential
election going back as far as 1924. We find that our instrument is also uncorrelated with
any of the earlier presidential election results. As discussed in Section 5, this result lends
additional support for our exclusion restriction. If our instrument merely captured uncontrolled
differences across counties, these should also correlate with vote shares in previous elections.
While these findings make it unlikely that our instrument is correlated with pro-
Democratic attitudes at the county level, a possible concern could be that we are picking up
“anti-populist” attitudes, which could have harmed Trump’s electoral results. To address this
concern, we turn to the historical case study of Ross Perot’s political campaign in 1992 and
1996. Perot, a billionaire businessman, also ran as a “third-party candidate” on a populist
platform. However, when we replace the dependent variable with the third party vote share
in the 1992 and 1996 presidential election (see Appendix Table C.6), we find no evidence that
our instrument is associated with lower vote shares for Ross Perot. This makes it unlikely we
are capturing differences in “demand for populism” across counties.40

Effects on Vote Share Changes. We also consider specifications of equations (2) and (3)
using vote share changes instead of levels as the dependent variable. All our estimates based
on changes take differences relative to the base year 2000 (akin to the approach in Autor et al.
(2020)) and use the full set of controls (as in columns 5 and 10 of Table 2). Figure 7a plots
the reduced form estimates for changes in the Republican vote share in presidential elections.
The results corroborate the previously presented evidence based on specifications in
levels. Our instrument is essentially uncorrelated not only with levels but also with changes
(or trends) in election outcomes during the 2000-2012 period. Given our arguments above,
this also lends support for our identification strategy. Again the estimates for 2000, 2004,
2008, and 2012 are also jointly indistinguishable from 0 (p-value=0.398). The reduced form
effects for 2016 and 2020 are similar to the one estimated using levels. For example, the
estimated effect (θ) for 2016 using changes is -0.017, similar to the one estimated in levels
(-0.021).41 Unsurprisingly, the average effect in 2016 and 2020 is statistically different from
the average effect in 2000, 2004, 2008, and 2012 (p-value=0.001).
40
In Appendix Table C.7, we also investigate the vote shares in the 2020 democratic primaries. Here we
find a positive association between Twitter exposure and the vote share of Bernie Sanders, often described as
a left-wing populist. This further speaks against the hypothesis of “anti-populist” sentiment.
41
Appendix Table C.4 present the OLS and 2SLS estimates.

22
Effects on Turnout and Congressional Elections. Figure 7b, Figure 8a, and Figure 8b
replicate the exercise in Figure 7a using voter turnout and vote shares in House and Senate
elections as the outcomes. We do not find a statistically significant association between our
instrument and election turnout except for 2020. Before 2020, the estimated point effects
are usually small. For example, the upper bound on the 95% confidence interval of the 2SLS
estimate for the effect of turnout in the 2016 election implies that a 10% increase in Twitter
users raises turnout by 0.036 p.p. (Appendix Table D.2).
In the 2020 election, which saw the highest turnout rate in more than a century (NPR,
2020c), we find that Twitter is associated with a larger fraction of the voting age population
casting their ballot. Why did Twitter have an effect on 2020 turnout but not in the previous
election? One possible explanation could be that calls to turn out and vote were widespread
on the platform, partially because of an initiative by Twitter itself that was not present in
2016 (Twitter, 2020). Further, the 2020 election was unique in its prevalence of mail and
early voting because of the Covid-19 pandemic. Another possibility is that Twitter served as
platform to convey information on how to vote by mail or before election day.
The coefficients for House and Senate races are more noisily estimated, particularly for
the smaller sample of Senate races (where only a third of seats is renewed every two years).
Overall, there is little evidence suggesting an effect of Twitter on congressional elections,
including in 2016, the 2018 midterm election, and 2020. Finding an effect on presidential vote
shares but not in these “down-ballot” races is perhaps expected since content on presidential
candidates (and in particular on Trump in 2016 and 2020) is more common on Twitter than
content on congressional races.42

Discussion of Identification Condition. As discussed in more detail in Section 5, there

are five pieces of evidence supporting our identification condition: i) our empirical strategy
compares relatively similar counties (Section 5); ii) the placebo test based on the coefficient on
SXSWcP re and other festivals; iii) the instrument being uncorrelated with election outcomes
in the 1924-2012 period; and iv) evidence suggesting Twitter has not affected House and
Senate races; while at the same time v) the instrument being correlated with vote choices of
moderate voters in particular.
Given this, a violation of the identification condition would require an omitted variable
that correlates with the instrument, Twitter usage, and Trump’s vote share in 2016 and 2020
but is uncorrelated with: i) SXSWcP re and other festivals, ii) levels and trends in election
results before Twitter’s launch and rise to popularity, iii) the observable variables presented in
42
Appendix Table D.3 presents the 2SLS estimates for House and Senate races. To accommodate the
Senate’s six-year terms, we take changes relative to 2000, 1998, and 1996, instead of always using 2000 (as we
do for other outcomes).

23
Table A.5, and iv) election results in congressional elections. At the same time, such omitted
variable would also v) be more strongly correlated with the vote choices of moderates and
independents than “partisans.” Our argument that is the existence of such omitted variable
is unlikely.

Towards an Average Treatment Effect As with any instrument, our 2SLS results
identify a local average treatment effect (LATE). In our setting, the “compliers” are counties
with higher Twitter usage as a result of the inflow of SXSW attendees. While the negative
treatment effect of Twitter usage for these counties is in itself an interesting finding, the
ATE for the US overall—and therefore the overall impact of Twitter on elections—may differ.
However, two pieces of evidence suggest that our estimates, despite this concern, allow us to
infer information about the ATE.
A first indication comes from comparing the OLS and 2SLS results in Table 2. Both
estimates are always negative and relatively similar in magnitude. Second, we build on
the approach suggested by Andrews and Oster (2019) and more formally investigate the
external validity bias of our estimates. For experimental settings, Andrews and Oster
(2019) suggest using the observable heterogeneity in estimated treatment effects within the
experimental sample to learn about the ATE in the overall population. Similarly, we can
use the heterogeneity of treatment effects within counties that provide the variation that
identify our results to approximate the ATE for the United States as a whole. Using all
included control variables from our main specification for the prediction of heterogeneity of
the treatment effect, the Andrews and Oster (2019) approach suggests that the ATE should, if
anything, be larger than the LATE we estimate in our baseline results. This seems plausible as
the more urban counties for which we have variation in our instrument tend to be Democratic
strongholds, and thus likely have fewer independents, for which we find the largest persuasion
effects (in survey data). We provide additional details on our approach in Appendix E43

6.2 Effects Are Concentrated Among Moderate Voters

If social media indeed matters for election outcomes, we would expect there to be heterogeneous
effects across groups of voters. In particular, Bayesian updating suggest that voters who
do not hold strong priors about a particular party should be more likely to be persuaded.
We test this prediction using individuals’ voting decision from the 2016 and 2020 CCES. In
43
As our setting differs from the one discussed in Andrews and Oster (2019), some adjustments to our
baseline estimation were required. We estimate the treatment effect exclusively in the subset of counties for
which either SXSWcM arch 2007 or SXSWcP re are not equal to zero. Then, we define a treatment indicator
variable equal to 1 for counties with SXSW followers who joined in March 2007.

24
particular, we estimate the following instrumental variable Probit regression:

yic = φ + θ · T witter
\ usersc + π · SXSWcP re + Xic ρ + εic , (4)

where yic is an indicator variable equal to 1 if an individual i living in county c voted for
Trump in the 2016 or 2020 election and 0 for Clinton.44 The definition of the county-level
variables T witter usagec and SXSWcP re remains unchanged. Xic is now a vector of individual-
level control variables including age, gender, race, family income, and education. We again
instrument for county-level Twitter usage based on the SXSW followers who joined in March
2007.
Table 4 presents results from estimating equation (4). In Panel A and B, Column
(1) suggests county-level Twitter usage has a statistically significant negative effect on the
likelihood to vote for Trump. The marginal effect implies that a 10% increase in the number
of Twitter users in a county would lower Trump’s vote share by 0.49 p.p. in the 2016 and
0.46 p.p. in the 2020 presidential election.45
Columns (2)-(6) report results estimated separately by voters’ reported party affiliation.
The effect is strongest for voters who identify as independents, and thus likely to not hold
strong priors. The results suggest weaker or zero effects for those with stronger political views,
whether Republican or Democrat.
These results on party affiliation further vary with age in a way that may be suggestive
of a role for social media. In the Online Appendix, Table D.4 repeats the 2016 exercise of
splitting CCES respondents by their party affiliation, but further divide voters into those
below and above 50. Since Twitter users tend to be younger than the general population
(Section 2), one may expect larger effects among voters below 50.
Indeed, we find larger marginal effects for Log(T witter users) among younger voters.
For independents, the estimate for young voters is 5% larger than for older voters (−0.079
compared to −0.071). Among moderate Republicans and Democrats, the estimated coefficients
are close to zero for voters aged 50+ but sizeable (at least twice as large) for younger voters
(although they are not statistically significant at conventional levels). Because young voters
are less likely to vote for Trump, this implies larger elasticities of vote outcomes with respect
to Twitter for those below 50 relative to the baseline probabilities.
44
Note that we use data on all CCES respondents, not only those in the 2016 wave (i.e., respondents from
the 2018 and 2020 waves were asked how they voted in 2016). The results are similar if we only use the 2016
wave. In unreported regressions, we do not find an effect on votes for Jill Stein.
45
This effect size is within the range of the county-level estimates presented in Table 2. Appendix Table C.8
shows this baseline result is robust to using only individuals with validated turnout and/or who stated that
they originally intended to vote for Trump in the pre-election wave of the CCES.

25
 A potential concern with this exercise is that party affiliation may itself be affected
by Twitter usage. We thus present further support for Twitter having persuasion effects on
moderates using county-level data that is not subject to such concerns. In particular, we
estimate our county-level specification (equation 3) for the 2016 elections, but split counties
based on how consistently they voted for either the Republican or the Democratic party.
Specifically, we define “swing counties” as counties that were not consistently won by one
party in the presidential elections from 2000 to 2012. Because we find no effect of Twitter on
vote shares before 2016 (see Table 3), splitting the sample by swing counties is not subject to
potential concerns that this variable might itself be affected by social media.
Appendix Table D.5 shows the results. For the 2016 presidential elections, we find that
Twitter usage only negatively impacts the Republican vote share in “swing” counties. We find
no evidence for Republican or Democratic strongholds, where people likely have the strongest
priors. The patterns are similar in 2020. But here, we also find a small effect on counties that
usually vote Republican, which could suggest that the effect on moderate Republicans we
find in the CCES also apply to the county level.

6.3 Potential Mechanisms

The findings above suggest that Twitter had an effect in 2016 and 2020, but not during
previous presidential elections. We address three potential explanations for this pattern: lack
of familiarity with social media (a learning channel ), changes in social media’s “slant” (a
content channel ), and Trump’s role as an outsider candidate (a political shock channel ).
The first factor could be the reach of and familiarity with social media content. In 2008,
social media was a relatively new type of technology. Only a quarter of American adults used
any social media platform and only 10% of internet users posted political commentary on
social media (Pew Research Center, 2009, 2011). Figure A.1a shows that Twitter, which was
founded in 2006, only had around one million users during the 2008 elections, compared to 40
million in 2012, 67 million in 2016, and 69 million in 2020 (Statista, 2019, 2020). Twitter’s
limited reach and novelty might have initially restricted its impact on voters.
The second possible explanation is that social media’s content changed between 2008
and 2016. It is conceivable that, in line with changes in the slant of cable news (e.g., Martin
and Yurukoglu, 2017), the typical content to which Twitter users are exposed has become
more left-leaning over time.
A third reason is that Trump’s political rise constituted a considerable shock to the
U.S. political system. In this view, Twitter may not have partisan effects per se. Instead, the
platform may have served as a conduit for spreading sentiments or information about Trump.

26
 Two pieces of evidence presented above are consistent with the political shock channel.
First, if Twitter had little effect before 2016 because it was not widely used, its effect on vote
shares should systematically increase over time. We do not find evidence supporting this idea
in effects for presidential, House, and Senate elections (Figures 7 and 8). Instead, we find a
discontinuous negative effect in the 2016 presidential election that persists in 2020. Second,
we do not find substantial effects for the 2016, 2018, and 2020 House and Senate elections.
This implies that Twitter usage lowered Trump’s vote share without substantially affecting
other Republican candidates on the same election day.

Results from the 2016 Republican Primaries. We provide additional evidence for a
Trump-specific effect of Twitter exposure by investigating the 2016 county-level Republican
primaries results. The primaries allow us to focus on the favorability of different candidates
among Republican voters. The results from this exercise are presented in Table 5. We find that
Twitter usage is associated with a lower vote share for Trump. We also find a positive effect
on the vote share of John Kasich, the most moderate of the major Republican candidates.46

Results from Gallup Approval Ratings. A similar pattern emerges when we use data
from the Gallup Daily Tracker, which contains approval ratings for three other Republican
presidential candidates who ran alongside Trump during the primaries (Ted Cruz, Marco
Rubio, and Kasich). Table 6 shows the results of running individual-level IV probit regressions
as in equation (4), where the dependent variable is an indicator variable equal to one if
the respondent approved of a specific candidate. As in Table 4, we differentiate between
respondents’ political affiliation.47
Table 6 confirms our main county-level result from general elections and primaries:
Twitter usage is associated lower approval of Trump, especially among independents (who are
presumably more likely to be persuaded by social media content). We also find lower approval
of Cruz, who is substantially more right-wing than other presidential primary candidates in
recent years (FiveThirtyEight, 2015). We find no link between Twitter use and approval of
the more moderate Republican candidates, Rubio and Kasich. For the Democrat candidates,
we find an effect for Clinton’s but not Sanders’ approval.48
46
In Appendix Table C.7, we also investigate the voting behavior in the 2016 and 2020 Democratic primaries.
Twitter usage appears to be associated with a higher support for Bernie Sanders in 2020.
47
We pool people who identify as leaning Republicans or Democrats with independents, because—in contrast
to the CCES data—only a few individuals in the survey are classified as “leaners.”
48
Note that Appendix Table C.7 reports a significant positive effect for Sanders county-level primary vote
share in 2020, but not in 2016. As mentioned earlier, Gallup Daily Tracker data is not available for 2020 at
the time of writing. Note also that Gallup collects data on candidate approval (as opposed to vote choice)
for the entire population (as opposed to Democratic primaries votes), which may explain some differences
between these results and the ones based on county-level primaries results.

27
 Taken together, these results are consistent with Twitter turning voters against voting
for Trump in particular and not against the Republican party more generally. Our results
may also explain the absence of an effect in the 2008 and 2012 elections: Obama’s opponents
John McCain and Mitt Romney were widely considered to be moderate Republicans (e.g.,
more similar to Kasich than Trump or Cruz).49

Slant of Election-Related Tweets. We provide further support for the hypothesis that
Trump’s 2016 campaign and presidency triggered opposition on Twitter by analyzing the
content of more than 460 million tweets mentioning the last name of presidential candidates
during the 2012, 2016, and 2020 presidential campaigns.
First, we classify the tweets’ slant as Republican, Democrat, or neutral using two
approaches described in Section 3. In the first case, we classify the political affiliation of
Twitter users by counting the number of Democrat and Republican Congress members they
follow. If a user follows more Democrats than Republicans, they are classified as Democrat,
and vice-versa. Tweets sent by a user classified as Democrat are classified as Democrat,
and so forth.50 In the second case, we classify individual tweets (not users) following an
approach in the spirit of Gentzkow and Shapiro (2010) and train a L2 regularized logistic
regression classifier to predict whether a tweet is more likely to have a Democrat or Republican
slant, depending on its content’s similarity to tweets sent by Congress members. If a tweet’s
content has higher similarity with those of Democratic Congress members, it is classified as
Democratic, and as Republican otherwise.51
Figure 9 plots the amount of Twitter attention directed at the Republican and Democratic
presidential candidates in the 2012, 2016, and 2020 elections, as well as the tweets’ estimated
slant based on users’ following of Congress members. To account for the attention and
popularity of tweets, we base the graphs on the number of “likes” the tweets mentioning the
last name of candidates received. In Appendix Figure D.1, we confirm that the results are
similar using the number of tweets and when we base the slant classification on the text of
the tweets (Figure D.2).52
49
As previously discussed, the number of Twitter users in 2008 was relatively small, but by 2012, it was
relatively close to its 2016 level (Figure A.1a).
50
Users who follow an equal number of Democrats and Republican or no Congress members are classified
as neutral. This approach is similar in spirit to Barberá (2015), who uses the network of Twitter users to
create a measure of ideology. Because we are only interested in a binary measure of partisan slant and not
the ideological distance of users, we do not estimate the full Bayesian ideal point mode. The advantage of our
simplified approach is that it is faster to compute and the resulting measure is easier to interpret.
51
See Section 3 and Appendix A.1. in the Appendix for more details. In unreported robustness checks, we
confirm that these findings are robust using different slant cutoffs for classifying Republican and Democratic
tweets, such as only classifying tweets for which the class probabilities are above 75% or 90%.
52
Twitter users can choose to “like” each tweet they see in their “timeline.” A user can only “like” a
particular tweet once. “Likes” thus provide an useful metric since they capture the popularity and attention

28
 Panel A shows the number of “likes” for tweets mentioning the Republican presidential
candidate (Romney and Trump), while Panel B provides similar evidence for the Democrats
(Obama, Clinton, and Biden). There are three noteworthy facts presented in the figure. First,
there was a sizable growth in the overall volume of Twitter content mentioning presidential
candidates. Second, the number of “likes” for tweets mentioning Trump is larger than those
mentioning his opponents (the difference is fourfold in 2016 and almost threefold in 2020).
Note that a “like” for a tweet mentioning a candidate can occur for tweets that are positive
or negative about the candidate, so the overall size of the bars are not informative about slant
or sentiments of Twitter content.
Third, Figure 9 also breaks down the share of tweets mentioning the candidates by
slant. The content sent by users classified as Democrats are more sizable than that from
those classified as Republicans. In particular, the amount of attention (proxied via “likes”)
on Twitter content mentioning Trump posted by Democrats is almost twice as large as the
amount posted from Republicans (for both 2016 and 2020). On the other hand, content on
Biden was more likely to have a Democrat slant and content on Clinton was almost equally
likely to have a Democrat or Republican slant.
Overall, a potential interpretation of these results is as follows. Twitter users, and users
of other social media platforms, are more likely to be young, well-educated, live in dense urban
areas, and support the Democratic party (see discussion in Section 2). Perhaps as a result,
Democratic politicians are more popular on Twitter than Republicans (Figure 1). In 2016 and
2020, Twitter became a vehicle for spreading opinions, particularly from Democratic-slanted
users, on Trump. This may, in turn, have persuaded voters with weaker priors—independents
and perhaps more moderate Republicans—to vote against Trump in the presidential election.53

7 Conclusion
Election officials around the globe are concerned about social media’s increasing influence
on voting decisions (e.g. NPR, 2020a). At the time of writing, there is a heated debate
about whether platform providers should “moderate” election-related content in the U.S. (e.g.
Politico, 2020). Exploiting variation based on a shock to Twitter’s initial rise to popularity,
that the content received. For example, if an account sent millions of Republican-slanted tweets about Clinton,
but such account had few followers and thus few users who can “like” the message, it would not meaningfully
affect measures based on “likes,” but could potentially do so for measures based on number of tweets.
53
Note that the 2012 election differed from 2016 and 2020 both in terms of having less twitter content
referring to it (as Figure D.2 indicates) and also for having a more moderate Republican candidate (Romney)
that may not have attracted as much Democrat-slanted content.

29
our paper provides some of the first empirical evidence that social media can affect election
outcomes.
We find that Twitter lowered the Republican party’s vote share in the 2016 and 2020
presidential elections. While this finding runs counter to a popular narrative that places social
media at the heart of Trump’s election win, it is consistent with a growing body of evidence
showing that social media users were less, not more likely to vote for Trump in 2016 or hold
polarized views (Boxell et al., 2017, 2018).
We also provide support for the idea that the demographics of Twitter users may account
for the platform’s partisan effects. People who use Twitter are 25 percentage points more
likely to identify as Democrats rather than Republican, and Democratic politicians are more
popular on Twitter than Republican ones. Our work suggests that this environment not only
reflects selection of like-minded individuals, but also affects voting decisions, particularly for
people with more moderate views.

30
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 Figure 1: Twitter Reach by Party

(a) Average # of tweets (per account) (b) Average # of followers (per account)

5,000 600,000

4,000
400,000
3,000

2,000
200,000
1,000

0 0
Democrat Republican Democrat Republican

(c) Average # of likes (per tweet) (d) Average # of retweets (per tweet)

800
400
600
300

400
200

200 100

0 0
Democrat Republican Democrat Republican

Notes: This figure plots data on the Twitter reach of Congress members. The sample includes all 901
senators and House representatives who were in office between 2007 and 2019 for whom we could identify
a Twitter account. For each account, we plot the average number of tweets and followers, and the
average number of “likes” and retweets of their tweets. Appendix Figure A.2 replicates the figure using
medians instead of averages. The data were collected from Twitter in November 2019.

39
 Figure 2: Republican Vote Share in the 2016 Presidential Election

1st Quintile
2nd Quintile
3rd Quintile
4th Quintile
5th Quintile

Notes: This map plots the geographical distribution of the Republican two-party vote share in the 2016
presidential election.

40
 Figure 3: Twitter Usage and Identifying Variation
(a) Twitter Usage per Capita

1st Quintile
2nd Quintile
3rd Quintile
4th Quintile
5th Quintile

(b) Identifying Variation

SXSW followers March 2007

SXSW followers pre-period
SXSW followers March 2007 & pre-period
No SXSW followers

Notes: This map plots the proxy for social media usage based on data from Twitter and the
identifying variation of our instrument. Panel (a) plots quintiles of the number of Twitter users
per capita. Panel (b) plots the three types of counties relevant for our identification strategy: 1)
the 47 counties with SXSW followers that joined Twitter both in March 2007 and the “pre-period”
(light red); 2) the 108 counties with SXSW followers that joined in March 2007, but none in the
“pre-period” (dark red); and 3) the 20 counties with SXSW followers that joined in the “pre-period,”
but none in March 2007 (blue).

41
Figure 4: South by Southwest (SXSW) 2007 and the Spread of Twitter
(a) Twitter Activity Around SXSW 2007

400 40,000
SXSW tweets (left axis) All tweets (right axis)

300 30,000

200 20,000

100 10,000

0 0
January February March April May

(b) SXSW and Local Twitter Adoption

0.15 SXSW festival starts

Change in Twitter activity (in SD)

0.10

0.05

0.00

-0.05
3

9
10

11

12

13

14
ek

ek

ek

ek

ek

ek

ek
ek

ek

ek

ek

ek
we

we

we

we

we

we

we
we

we

we

we

we
07

07

07

07

07

07

07
07

07

07

07

07
20

20

20

20

20

20

20
20

20

20

20

20

Notes: Panel (a) plots the total number of tweets and the number of tweets containing the term
“SXSW” over time, smoothed using a 7-day moving average. Panel (b) plots the estimates of βτ from
the panel event study regression tweetsct = τ βτ SXSWcM arch2007 ×1(t = τ )+ τ δτ SXSWcP re ×
P P
1(t = τ ) + θc + γt + εct where tweetsct is the log of (one plus) the number of tweets in county c on
week t, SXSWcM arch2007 is the logarithm of (one plus) the number of SXSW followers in county
c that joined Twitter on March 2007 and SXSWcP re is a similarly defined variable for followers
that joined Twitter before March 2007. We standardize the variables to have a mean of zero and
standard deviation of one. The whiskers represent 95% confidence intervals based on standard
errors clustered by state.

42
Figure 5: First Stage – South by Southwest (SXSW) and Twitter Usage

0.50
Log(Twitter users), residualized

0.25

0.00

-0.25

-0.50

-1.00 -0.50 0.00 0.50 1.00

Log(1 + SXSW followers, March 2007), residualized

Notes: This figure presents a binned scatter plot of the relationship between Twitter users in
2014-2015 and the number of SXSW followers who joined Twitter in March 2007. Variables are
residualized by partialling out SXSW followers who joined before March 2007, population deciles,
Census region fixed effects, as well as geographical, demographic, socioeconomic, China shock, and
1996 election control variables (see Online Appendix for control variable definitions). The figure is
constructed by dividing the x-axis variable into 40 equal-sized bins and plotting the average values
of both variables in each bin. The fitted line is estimated using the unbinned data.

43
 Figure 6: South by Southwest, Twitter, and the Republican Vote Share

2016 Election
(a) Reduced Form (b) OLS
0.02
0.02
Republican vote share, 2016 (residualized)

Republican vote share, 2016 (residualized)

0.01
0.01

0.00
0.00

-0.01

-0.01

-0.02

-0.02
-1.00 -0.50 0.00 0.50 1.00 -1.50 -1.00 -0.50 0.00 0.50 1.00

Log(1 + SXSW followers, March 2007), residualized Log(Twitter users), residualized

2020 Election
(c) Reduced Form (d) OLS
0.04
Republican vote share, 2020 (residualized)

Republican vote share, 2020 (residualized)

0.02

0.01
0.02

0.00

0.00
-0.01

-0.02

-0.02
-1.00 -0.50 0.00 0.50 1.00 -1.50 -1.00 -0.50 0.00 0.50 1.00

Log(1 + SXSW followers, March 2007), residualized Log(Twitter users), residualized

Notes: Panel (a) presents a binned scatter plot of the relationship between the Republican vote share in
the 2016 presidential election and the number of SXSW followers who joined Twitter in March 2007.
Variables are residualized with respect to SXSW followers who joined before March 2007, population
deciles, Census region fixed effects, as well as geographical, demographic, socioeconomic, China shock,
and 1996 election control variables. The figure is constructed by dividing the x-axis variable into 40
equal-sized bins and plotting the average values of both variables in each bin. The fitted line is estimated
using the unbinned data. Panels (b) and (d) replicate the exercise using Twitter users in 2014-2015 as
the x-axis variable. Panel (c) and (d) show results for the 2020 election.

44
 Figure 7: Twitter and Presidential Elections – Reduced Form
(a) Changes in Republican Vote Share

0.005

Point estimate on Log(SXSW followers, March 2007)

0.000

-0.005

-0.010

-0.015

2000-04 2000-08 2000-12 2000-16 2000-20

(b) Change in Voter Turnout

0.015
Point estimate on Log(SXSW followers, March 2007)

0.010

0.005

0.000

-0.005

-0.010
2000-04 2000-08 2000-12 2000-16 2000-20

Notes: These figures plot reduced form estimates β̂ 0 from county-level regressions as in equation (2).
They measure the effect of Log(1 + SXSW followers, March 2007), while controlling for Log(1 + SXSW
followers, Pre), on changes in the Republican vote share in presidential elections relative to the year
2000 in Panel (a), and changes in the ratio of voter turnout to voting-age population relative to 2000 in
Panel (b). All regressions control for population deciles and Census region fixed effects, and the full
set of controls (as in columns 5 and 10 of Table 2). On Panel (a), the estimates for 2004, 2008, and
2012 are jointly statistically insignificant (p-value=0.398). Further, the average effect in 2016 and 2020
is statistically distinct from the average effect for 2004, 2008, and 2012 (p-value=0.001). Regressions
are weighted by turnout in the 2000 presidential election. Whiskers represent 95% confidence intervals
based on standard errors clustered by state.
45
 Figure 8: Twitter and Congressional Election Results – Reduced Form
(a) House Elections

0.06
Point estimate on Log(SXSW followers, March 2007)

0.04

0.02

0.00

-0.02
2000-02 2000-04 2000-06 2000-08 2000-10 2000-12 2000-14 2000-16 2000-18 2000-20

(b) Senate Elections

0.04
Point estimate on Log(SXSW followers, March 2007)

0.02

0.00

-0.02

-0.04

1996-02 1998-04 2000-06 1996-08 1998-10 2000-12 1996-14 1998-16 2000-18 1996-20

Notes: These figures plot reduced form estimates β̂ 0 from county-level regressions as in equation (2).
They measure the reduced form effect of Log(1 + SXSW followers, March 2007), while controlling for
Log(1 + SXSW followers, Pre), on the Republican vote share in House and Senate elections since 2000.
For House elections in Panel (a), the dependent variable is the change in the Republican vote share since
2000. For Senate elections in Panel (b), the dependent variable is the change in the Republican vote
share from six, twelve, or eighteen years ago (to accommodate senators’ 6-year terms). All regressions
control for population deciles and Census region fixed effects and the full set of controls (as in columns 5
and 10 of Table 2). Regressions are weighted by turnout in the 2000 presidential election. The whiskers
represent 95% confidence intervals based on standard errors clustered by state.

46
 Figure 9: Twitter’s Partisan Slant Across Presidential Elections
(a) Tweets about Republican Presidential Candidates

24%
600
26%
Number of Likes (in millions) 50%

500

400

300

200 15%
26%
100 27% 59%
11%
0
62%
Election 2012 Election 2016 Election 2020

Democratic Slant Republican Slant Neutral Slant

(b) Tweets about Democratic Presidential Candidates

600
Number of Likes (in millions)

500

400

300 18%
37%
200 45%

14%
100 29%
44%
23%
42%
0
48%
Election 2012 Election 2016 Election 2020

Democratic Slant Republican Slant Neutral Slant

Notes: These figures present the number of “likes” received by tweets that contain the last name of
the candidates in the 2012, 2016 and 2020 presidential elections, depending on whether the tweet was
classified as having a Republican, Democratic, or neutral slant. We classify the slant of a tweet based on
the Twitter network of the user who sent the tweet. If the user follows more Democratic than Republican
Congress members, they will be classified as a Democrat, and vice versa. Users who follow an equal
number of Democrats and Republican or no Congress members are classified as neutral.

47
 Table 1: South by Southwest 2007 and Twitter Usage

Dep. var.: Log(Twitter users)

(1) (2) (3) (4) (5)
Log(SXSW followers, March 2007) 0.726*** 0.683*** 0.563*** 0.524*** 0.523***
(0.087) (0.079) (0.055) (0.048) (0.048)
Log(SXSW followers, Pre) 0.104 0.110 0.059 0.059 0.058
(0.101) (0.076) (0.098) (0.082) (0.082)
Population deciles Yes Yes Yes Yes Yes
Census region FE Yes Yes Yes Yes Yes
Geographical controls Yes Yes Yes Yes
Demographic controls Yes Yes Yes
Socioeconomic controls Yes Yes Yes
China shock controls Yes Yes
1996 election control Yes
Observations 3,065 3,065 3,064 3,064 3,064
R2 0.92 0.93 0.95 0.95 0.95
Mean of DV 8.22 8.22 8.22 8.22 8.22
p-value: March 2007 = Pre 0.00 0.00 0.00 0.00 0.00
Notes: This table presents county-level regressions where the dependent variable is the number
of Twitter users (in natural logarithm). Log(SXSW followers, March 2007) is the number of
Twitter users (in logs, with 1 added inside) who joined in March 2007 and follow South by
Southwest (SXSW). SXSW followers, Pre is the number of SXSW followers who registered at
some point in 2006, defined similarly. The bottom row reports p-values from F-tests for the
equality of these coefficients. Regressions include the indicated control variables (see the Online
Appendix for their descriptions). Observations are weighted by turnout in the 2000 presidential
election. Standard errors in parentheses are clustered by state. *** p < 0.01, ** p < 0.05, *
p < 0.1.

48
 Table 2: Twitter and the 2016/2020 Republican Vote Share

Dep. var.: Republican vote share in 2016 Dep. var.: Republican vote share in 2020
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Panel A: OLS
Log(Twitter users) -0.065*** -0.067*** -0.013*** -0.011*** -0.007** -0.058*** -0.064*** -0.012*** -0.011*** -0.008***
(0.009) (0.008) (0.004) (0.003) (0.003) (0.009) (0.009) (0.004) (0.003) (0.003)
Panel B: Reduced form
Log(SXSW followers, March 2007) -0.053*** -0.058*** -0.019*** -0.014*** -0.011*** -0.046*** -0.055*** -0.017*** -0.013*** -0.011**
(0.011) (0.012) (0.005) (0.004) (0.004) (0.009) (0.010) (0.006) (0.005) (0.005)
Log(SXSW followers, Pre) -0.021 -0.003 -0.000 -0.002 0.001 -0.022 -0.005 -0.002 -0.004 -0.001
(0.016) (0.013) (0.006) (0.006) (0.004) (0.016) (0.013) (0.007) (0.007) (0.005)
Panel C: 2SLS
Log(Twitter users) -0.072*** -0.085*** -0.034*** -0.027*** -0.021*** -0.064*** -0.080*** -0.031** -0.025*** -0.020**
(0.016) (0.018) (0.010) (0.008) (0.008) (0.015) (0.017) (0.011) (0.009) (0.009)
Log(SXSW followers, Pre) -0.014 0.007 0.002 -0.001 0.002 -0.015 0.004 -0.000 -0.002 0.000

49
(0.020) (0.016) (0.007) (0.006) (0.005) (0.020) (0.015) (0.008) (0.007) (0.006)
Population deciles Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Census region FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Geographical controls Yes Yes Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes
Socioeconomic controls Yes Yes Yes Yes Yes Yes
China shock controls Yes Yes Yes Yes
1996 election control Yes Yes
Observations 3,065 3,065 3,064 3,064 3,064 3,065 3,065 3,064 3,064 3,064
Mean of DV 0.46 0.46 0.46 0.46 0.46 0.47 0.47 0.47 0.47 0.47
Robust F-stat. 69.57 74.65 106.08 118.21 121.18 69.57 74.65 106.08 118.21 121.18
Notes: This table presents county-level regressions where the dependent variable is the Republican vote share in the 2016 or 2020 presidential election. Log(SXSW followers,
March 2007) is the number of Twitter users (in logs, with 1 added inside) who joined in March 2007 and follow South by Southwest (SXSW). SXSW followers, Pre is the
number of SXSW followers who registered at some point in 2006, defined similarly. Twitter users are the number of users in 2014-2015. Regressions include the indicated
control variables (see the Online Appendix for their descriptions). The first-stage regressions for 2SLS results (Panel B) are presented in Table 1, with the F-stat for the
excluded instrument in the bottom row. The “Oster-δ” for gauging the possible role of unobservables in driving coefficient stability is 6.93 (comparing column 5 to 1) and
7.02 (comparing column 10 to 6); see text for further details. Observations are weighted by turnout in the 2000 presidential election. Standard errors in parentheses are
clustered by state. *** p < 0.01, ** p < 0.05, * p < 0.1.
 Table 3: Twitter and the Republican Vote Share, 2000-2020

Dep. var.: Republican vote share in...

2000 2004 2008 2012 2016 2020
(1) (2) (3) (4) (5) (6)
Panel A: Reduced form
Log(SXSW followers, March 2007) -0.003 -0.005 -0.006 -0.003 -0.011*** -0.011**
(0.002) (0.003) (0.004) (0.004) (0.004) (0.005)
Log(SXSW followers, Pre) 0.001 0.001 -0.000 -0.002 0.001 -0.001
(0.004) (0.003) (0.006) (0.005) (0.004) (0.005)
Panel B: 2SLS
Log(Twitter users) -0.005 -0.009 -0.011 -0.007 -0.021*** -0.020**
(0.004) (0.006) (0.009) (0.008) (0.008) (0.009)
Log(SXSW followers, Pre) 0.001 0.001 0.000 -0.001 0.002 0.000
(0.004) (0.004) (0.006) (0.005) (0.005) (0.006)
Observations 3,064 3,064 3,064 3,064 3,064 3,064
Mean of DV 0.48 0.51 0.46 0.47 0.46 0.47
Robust F-stat. 121.18 121.18 121.18 121.18 121.18 121.18
Notes: This table presents county-level regressions where the dependent variable is the Republican
vote share in presidential elections. Log(SXSW followers, March 2007) is the number of Twitter users
(in logs, with 1 added inside) who joined in March 2007 and follow South by Southwest (SXSW).
SXSW followers, Pre is the number of SXSW followers who registered at some point in 2006, defined
similarly. Twitter users are the number of users in 2014-2015. All regressions control for population
deciles, Census region fixed effects, and the full set of controls (as in columns 5 and 10 of Table 2). The
first-stage regression for 2SLS results (Panel B) are presented in column (5) of Table 1, with the F-stat
for the excluded instrument in the bottom row. On Panel (a), the coefficients on Log(SXSW follow-
ers, March 2007) for 2000, 2004, 2008, and 2012 are jointly statistically insignificant (p-value=0.329).
Further, the average effect in 2016 and 2020 is statistically distinct from the average effect in 2000,
2004, 2008, and 2012 (p-value=0.002). Observations are weighted by turnout in the 2000 presidential
election. Standard errors in parentheses are clustered by state. *** p < 0.01, ** p < 0.05, * p < 0.1.

50
 Table 4: Twitter and Individuals’ Vote Decisions in 2016/2020

Dep. var.: Voted for Trump in ...

Full Strong Mod. Mod. Strong
Sample Dem. Dem. Indep. Rep. Rep.
(1) (2) (3) (4) (5) (6)
Panel A: 2016 Election
Log(Twitter users) -0.135*** 0.018 -0.079 -0.209*** -0.045 -0.022
(0.045) (0.054) (0.067) (0.058) (0.042) (0.037)
Marginal effect [-0.049] [0.001] [-0.014] [-0.073] [-0.006] [-0.001]
Observations 146,579 44,241 30,745 13,625 27,865 28,559
Mean of DV 0.492 0.026 0.111 0.611 0.919 0.980
Panel B: 2020 Election
Log(Twitter users) -0.130*** -0.069 -0.002 -0.186*** -0.003 -0.080
(0.042) (0.075) (0.120) (0.063) (0.040) (0.079)
Marginal effect [-0.046] [-0.002] [0.000] [-0.069] [-0.001] [-0.004]
Observations 43,617 13,108 9,670 4,430 7,388 8,395
Mean of DV 0.475 0.012 0.074 0.516 0.908 0.978
Notes: This table presents results estimated using IV probit models, as in equation (4). The
dependent variable is a dummy for individuals in the CCES who voted for Trump in 2016 or 2020.
Log(Twitter users) is instrumented using the (log) number of SXSW followers that joined Twitter
in March 2007. All regressions control for the (log) number of SXSW followers that joined Twitter
at some point in 2006, family income, gender, education levels, marital status, news interest, and
age, as well as county-level population deciles and Census region fixed effects. Regressions are
weighted by survey weights. Standard errors in parentheses are clustered by state. *** p < 0.01,
** p < 0.05, * p < 0.10.

51
Table 5: Twitter and Vote Shares in the 2016 Republican Primaries

Dep. var.: Vote share in Republican Primary of...

Trump Cruz Rubio Bush Kasich
(1) (2) (3) (4) (5)
Panel A: Reduced form
Log(SXSW followers, March 2007) -0.030** 0.004 0.016 -0.002 0.017**
(0.012) (0.009) (0.010) (0.001) (0.008)
Panel B: 2SLS
Log(Twitter users) -0.044** 0.005 0.024 -0.003 0.025**
(0.017) (0.013) (0.015) (0.002) (0.011)
Observations 2,831 2,831 2,831 2,831 2,831
Mean of DV 0.48 0.23 0.09 0.01 0.15
Robust F-stat. 69.54 69.54 69.54 69.54 69.54
Notes: This table presents county-level regressions where the dependent variable is the vote
share of the indicated candidate in the Republican party primaries in 2016. Log(SXSW
followers, March 2007) is the number of Twitter users (in logs, with 1 added inside) who
joined in March 2007 and follow South by Southwest (SXSW). SXSW followers, Pre is the
number of SXSW followers who registered at some point in 2006, defined similarly. Twitter
users are the number of users in 2014-2015. All regressions control for population deciles,
Census region fixed effects, and the full set of controls (as in columns 5 and 10 of Table 2).
The first-stage regressions for 2SLS results (Panel B) are analogous to the one presented in
Table 1, except for the different sample of counties for which primary results are available.
The F-stat for the excluded instrument is provided in the bottom row. Observations are
weighted by turnout in the 2000 presidential election. Standard errors in parentheses are
clustered by state. *** p < 0.01, ** p < 0.05, * p < 0.1.

52
Table 6: Twitter and Candidate Approval during the 2016 Primaries

Dep. var.: Approved of candidate during primaries

Trump Cruz Rubio Kasich Sanders Clinton
(1) (2) (3) (4) (5) (6)
Panel A: Republicans
Log(Twitter users) -0.108*** -0.086** -0.051 0.018 0.031 0.148***
(0.030) (0.035) (0.060) (0.050) (0.039) (0.041)
Marginal effect [-0.038] [-0.029] [-0.014] [0.006] [0.009] [0.022]
Observations 19,974 11,959 8,344 8,995 16,099 20,983
Mean of DV 0.647 0.698 0.779 0.665 0.238 0.092
Panel B: Independents and Leaners
Log(Twitter users) -0.065** -0.006 -0.015 0.050 0.059 0.154***
(0.028) (0.035) (0.043) (0.042) (0.043) (0.036)
Marginal effect [-0.021] [-0.002] [-0.006] [0.019] [0.021] [0.054]
Observations 22,852 12,135 8,080 8,280 17,356 23,813
Mean of DV 0.329 0.392 0.516 0.581 0.595 0.380
Panel C: Democrats
Log(Twitter users) -0.052 -0.116** -0.036 0.076 0.004 0.081**
(0.051) (0.054) (0.056) (0.051) (0.050) (0.038)
Marginal effect [-0.009] [-0.030] [-0.012] [0.029] [0.001] [0.021]
Observations 20,866 11,098 7,460 7,547 16,059 21,454
Mean of DV 0.107 0.195 0.271 0.502 0.808 0.807
This table presents results estimated using IV probit models, as in equation (4). The
dependent variable is a dummy for individuals who approved the respective presidential
candidate during the presidential primaries in 2015 and 2016. We restrict the sample to
the period before Trump became the presumptive nominee in June 2016. Log(Twitter
users) is instrumented using the number of SXSW followers that joined Twitter in March
2007. All regressions control for the (log) number of SXSW followers that joined Twitter at
some point in 2006, income, gender, education, and marital status, as well as county-level
population deciles and Census region fixed effects. Regressions are weighted by survey
weights. Standard errors in parentheses are clustered by state. *** p < 0.01, ** p < 0.05, *
p < 0.10.

53
 Online Appendix
This appendix presents further details on data construction, the SXSW festival, additional
robustness exercises, further results, and the LATE extrapolation:

• Appendix A provides additional details on the data.

• Appendix B discusses the SXSW instrument.

• Appendix C shows additional robustness checks.

• Appendix D provides further results.

• Appendix E discusses extrapolation for the average treatment effect.

A Additional Details on Data

Table A.1: List of Congress Members with the Most Twitter Followers

Rank Name Twitter Handle Party Tweets Followers Likes Retweets

1. Barack Obama barackobama D 15,754 114,369,395 38,496 8,475
2. Hillary Rodham Clinton hillaryclinton D 11,491 27,026,749 22,413 6,780
3. Bernard Sanders BernieSanders D 17,667 11,436,732 19,324 3,625
4. Alexandria Ocasio-Cortez AOC D 10,087 6,596,922 30,668 8,634
5. Elizabeth Warren SenWarren D 5,345 5,743,064 5,845 1,863
6. Joseph R. Biden Jr. joebiden D 4,228 4,507,744 11,789 2,472
7. Cory A. Booker CoryBooker D 65,242 4,468,595 2,802 798
8. Mike Pence mike pence R 8,069 4,397,219 2,768 4,176
9. Nancy Pelosi SpeakerPelosi D 10,154 4,149,922 9,022 2,693
10. Marco Rubio MarcoRubio R 12,789 4,098,788 2,642 1,608
11. Paul D. Ryan SpeakerRyan R 14,755 3,646,397 781 260
12. Kamala D. Harris KamalaHarris D 13,119 3,476,952 10,366 2,229
13. John F. Kerry johnkerry D 2,608 3,360,092 773 328
14. John McCain SenJohnMcCain R 14,409 3,075,281 2,551 721
15. Rand Paul randpaul R 13,642 2,700,813 3,926 1,491
16. Charles E. Schumer SenSchumer D 17,004 2,145,147 6,457 2,171
17. Adam B. Schiff RepAdamSchiff D 5,623 2,087,003 19,009 6,849
18. Ilhan Omar IlhanMN D 14,678 1,947,411 6,644 2,649
19. Lindsey Graham LindseyGrahamSC R 10,872 1,535,623 5,698 1,616
20. Mike Pompeo SecPompeo R 2,148 1,503,151 4,936 1,711
Notes: This table lists the 20 Congress members who were in office between 2007 and 2019 with the most Twitter follow-
ers at the time of data collection. Likes and retweets refer to the average number of interactions the Congress members
receive for their average tweet. The data were collected from Twitter in November 2019.

1
 Figure A.1: Validation of Twitter usage measure
(a) Twitter Usage over Time

80
Statista (right axis)
5
Number of Twitter users (in millions)

60
4

3
40

20
1 Gesis (left axis)

0 0
2007q1 2009q1 2011q1 2013q1 2015q1 2017q1 2019q1

(b) Twitter Usage by County (Gesis vs GfK)

12
Log(# of households using Twitter)

10

2 4 6 8 10

Log(Twitter users)

Notes: This graph shows two validation exercises for the Twitter usage measure in the Gesis data
(Kinder-Kurlanda et al., 2017). Panel (a) plots the number of Twitter users in the Gesis data
and the number of active monthly users reported by Statista based on Twitter’s own reporting.
Panel (b) plots the percentiles of the number of Twitter users in the Gesis data at the county-level
against the number of users based on the GfK Media Survey.

2
 Figure A.2: Twitter Reach by Party (Median)

(a) Median # of tweets (per account) (b) Median # of followers (per account)

4,000
25,000

3,000 20,000

15,000
2,000
10,000
1,000
5,000

0 0
Democrat Republican Democrat Republican

(c) Median # of likes (per tweet) (d) Median # of retweets (per tweet)

30 150

20 100

10 50

0 0
Democrat Republican Democrat Republican

Notes: This figure plots data on the Twitter reach of Congress members. The sample includes all 901
senators and House representatives who were in office between 2007 and 2019 for whom we could identify
a Twitter account. For each account, we plot the median number of tweets and followers, and the median
number of “likes” and retweets of their tweets. The data were collected from Twitter in November 2019.

3
 Figure A.3: News Reports About Social Media

4000
Twitter
Number of news reports on social media

Facebook

3000 2016 Election

2000 2012 Election

1000

0
1

1
m

m
04

06

08

10

12

14

16

18
20

20

20

20

20

20

20

20

Notes: This graph plots the number of times the terms “Twitter” and “Facebook” are
mentioned in USA Today, The Washington Post, The New York Post, and The New
York Times based on data from Nexis.

4
 Table A.2: Summary Statistics (County-Level)

Mean Std. Dev. Min. Median Max. N

Vote outcomes and Twitter data
Republican two-party vote share (2016) 0.46 0.17 0.08 0.45 0.95 3,065
Change in Republican two-party vote share, 2000-2016 -0.02 0.10 -0.33 -0.03 0.45 3,065
Republican two-party vote share (2020) 0.47 0.17 0.09 0.45 0.96 3,065
Change in Republican two-party vote share, 2000-2020 -0.01 0.10 -0.34 -0.02 0.48 3,065
Log(Twitter users) 8.22 1.99 0.00 8.45 12.35 3,065
Log(SXSW followers, March 2007) 0.69 1.13 0.00 0.00 4.98 3,065
Log(SXSW followers, Pre) 0.33 0.73 0.00 0.00 3.61 3,065
Geographical controls
Population density 1925.15 6342.94 0.10 508.30 69468.40 3,065
Log(County area) 6.72 0.92 3.26 6.64 9.91 3,065
Distance from Austin, TX (in miles) 1731.21 653.61 5.04 1750.86 3098.88 3,065
Distance from Chicago (in miles) 1246.18 813.51 7.16 1103.75 3040.38 3,065
Distance from NYC (in miles) 1600.47 1255.14 6.48 1285.98 4191.67 3,065
Distance from San Francisco (in miles) 2841.16 1231.98 41.11 3157.16 4565.01 3,065
Distance from Washington, DC (in miles) 1448.51 1175.55 7.88 1047.13 3983.08 3,065
Demographic controls
% aged 20-24 0.07 0.02 0.01 0.06 0.27 3,065
% aged 25-29 0.07 0.01 0.03 0.07 0.15 3,065
% aged 30-34 0.07 0.01 0.03 0.06 0.12 3,065
% aged 35-39 0.06 0.01 0.03 0.06 0.10 3,065
% aged 40-44 0.06 0.01 0.02 0.06 0.10 3,065
% aged 45-49 0.06 0.01 0.02 0.06 0.09 3,065
% aged 50+ 0.36 0.06 0.11 0.35 0.75 3,065
Population growth, 2000-2016 0.14 0.19 -0.43 0.10 1.32 3,065
% white 0.65 0.21 0.03 0.68 0.98 3,065
% black 0.12 0.12 0.00 0.08 0.85 3,065
% native American 0.01 0.03 0.00 0.00 0.90 3,065
% Asian 0.05 0.06 0.00 0.03 0.37 3,065
% Hispanic 0.15 0.15 0.01 0.09 0.96 3,065
% unemployed 5.31 1.42 1.80 5.10 24.10 3,065
Socioeconomic controls
% below poverty level 15.11 5.34 1.40 15.10 53.30 3,065
% employed in IT 0.02 0.02 0.00 0.02 0.21 3,065
% employed in construction/real estate 0.07 0.03 0.00 0.07 1.00 3,065
% employed in manufacturing 0.11 0.08 0.00 0.08 0.72 3,065
% adults with high school degree 28.13 7.41 8.30 27.50 54.80 3,065
% adults with college degree 20.98 3.66 8.70 20.90 35.60 3,065
% watching Fox News 0.26 0.01 0.23 0.26 0.30 3,064
% watching prime time TV 0.43 0.01 0.40 0.43 0.47 3,064
China shock controls
Exposure to Chinese import competition 2.63 2.02 -0.63 2.10 43.08 3,065
Share of routine occupations 31.87 2.36 22.23 32.14 36.66 3,065
Average offshorability index -0.02 0.50 -1.64 0.09 1.24 3,065
1996 election control
Republican two-party vote share (1996) 0.41 0.11 0.10 0.41 0.79 3,065
Notes: This table presents descriptive statistics for the main estimation sample, weighted by the turnout in the 2000
presidential elections.

5
 Table A.3: Summary Statistics (2016 CCES Individual-Level)

Mean Std. Dev. Min. Median Max. N

Vote outcome
Voted for Trump 0.49 0.50 0.00 0.00 1.00 146,579
Twitter data
Log(Twitter users) 8.29 1.91 0.69 8.44 12.34 146,579
Log(SXSW followers, March 2007) 0.69 1.12 0.00 0.00 4.98 146,579
Log(SXSW followers, Pre) 0.32 0.71 0.00 0.00 3.61 146,579
Individual control variables
Log(Age) 3.89 0.37 2.89 3.99 4.61 146,579
Family income dummies 7.12 3.65 1.00 7.00 13.00 146,579
Female dummy 1.52 0.50 1.00 2.00 2.00 146,579
Education dummies 3.54 1.54 1.00 3.00 6.00 146,579
Marital status dummies 2.36 1.71 1.00 1.00 5.00 146,579
Interest in news dummies 1.61 0.98 1.00 1.00 7.00 146,579
Notes: This table presents descriptive statistics for the CCES estimation sample, weighted by survey
weights.

Table A.4: Summary Statistics (Gallup Individual-Level)

Mean Std. Dev. Min. Median Max. N

Candidate approval outcomes
Approve of Trump? 0.34 0.48 0.00 0.00 1.00 64,764
Approve of Kasich? 0.60 0.49 0.00 1.00 1.00 8,735
Approve of Rubio? 0.50 0.50 0.00 1.00 1.00 6,201
Approve of Cruz? 0.41 0.49 0.00 0.00 1.00 11,504
Approve of Sanders? 0.57 0.50 0.00 1.00 1.00 27,137
Approve of Clinton? 0.43 0.50 0.00 0.00 1.00 36,367
Twitter data
Log(Twitter users) 8.29 1.97 0.00 8.48 12.35 64,764
Log(SXSW followers, March 2007) 0.72 1.15 0.00 0.00 4.98 64,764
Log(SXSW followers, Pre) 0.34 0.73 0.00 0.00 3.61 64,764
Individual control variables
Income dummies 6.99 2.38 1.00 7.00 10.00 64,764
Female dummy 1.50 0.50 1.00 2.00 2.00 64,764
Education dummies 3.58 1.60 1.00 4.00 6.00 64,764
Marital status dummies 1.98 0.94 1.00 2.00 5.00 64,764
Age deciles 4.45 2.68 1.00 4.00 10.00 64,764
Notes: This table presents descriptive statistics for the Gallup estimation sample, weighted by
survey weights.

6
 Table A.5: Instrument Balancedness

March 2007 March 2007 Pre Difference

and Pre only only in means Šidàk
(1) (2) (3) (2) - (3) p-value p-value
Population density 5192.27 1021.39 1998.35 -976.96 0.07* 0.91
Log(County area) 6.30 6.63 6.54 0.09 0.73 1.00
Distance from Austin, TX (in miles) 1775.99 1749.38 1626.64 122.74 0.48 1.00
Distance from Chicago (in miles) 1439.45 1329.47 1214.42 115.05 0.53 1.00
Distance from NYC (in miles) 1685.31 1594.99 1510.05 84.94 0.78 1.00
Distance from San Francisco (in miles) 2751.83 2900.11 2833.01 67.10 0.83 1.00
Distance from Washington, DC (in miles) 1558.55 1450.23 1397.05 53.18 0.85 1.00
% aged 20-24 0.07 0.08 0.08 0.00 0.92 1.00
% aged 25-29 0.09 0.07 0.07 -0.00 0.51 1.00
% aged 30-34 0.08 0.07 0.07 -0.00 0.58 1.00
% aged 35-39 0.07 0.06 0.06 -0.00 0.82 1.00
% aged 40-44 0.06 0.06 0.06 0.00 0.82 1.00
% aged 45-49 0.07 0.06 0.06 0.00 0.89 1.00
% aged 50+ 0.32 0.35 0.35 -0.00 0.97 1.00
Population growth, 2000-2016 0.18 0.18 0.15 0.03 0.56 1.00
% white 0.50 0.65 0.67 -0.02 0.62 1.00
% black 0.18 0.12 0.08 0.04 0.20 1.00
% native American 0.01 0.01 0.02 -0.02 0.02** 0.45
% Asian 0.10 0.05 0.05 -0.01 0.55 1.00
% Hispanic 0.20 0.16 0.15 0.01 0.80 1.00
% unemployed 4.86 5.05 4.51 0.54 0.07* 0.91
% below poverty level 15.71 15.82 13.69 2.14 0.17 1.00
% employed in IT 0.04 0.02 0.02 -0.00 0.98 1.00
% employed in construction/real estate 0.06 0.07 0.07 0.01 0.39 1.00
% employed in manufacturing 0.07 0.09 0.07 0.02 0.16 0.99
% adults with high school degree 21.76 25.99 25.77 0.22 0.88 1.00
% adults with college degree 18.89 21.16 21.20 -0.04 0.97 1.00
% watching Fox News 0.25 0.26 0.26 -0.00 0.91 1.00
% watching prime time TV 0.42 0.43 0.43 0.00 0.91 1.00
Exposure to Chinese import competition 2.55 2.46 2.79 -0.32 0.54 1.00
Share of routine occupations 32.47 31.38 31.25 0.13 0.82 1.00
Average offshorability index 0.37 -0.07 -0.05 -0.02 0.84 1.00
Republican two-party vote share (1996) 0.36 0.42 0.42 -0.00 0.90 1.00
Notes: This table presents the averages for the main control variables separately for the three types of counties
relevant for our identification strategy: 1) the 47 counties with SXSW followers that joined Twitter both in March
2007 and the “pre-period”; 2) the 108 counties with SXSW followers that joined in March 2007 (but none in the
“pre-period”); and 3) the 20 counties with SXSW that joined in the “pre-period” (but none in March 2007). The
demographic and socioeconomic controls are measured in 2016. We report p-values from a two-sided t-test for the
equality of means between the counties with the key identifying variation, as well as Šidàk-corrected values to
account for multiple hypothesis testing. *** p < 0.01, ** p < 0.05, * p < 0.1.

7
 Table A.6: Description of Main Variables

Variable Description Source

Vote outcomes
Republican vote share (2016/2020) The vote share of the Republican party in the 2016 or 2020 presidential election. Dave Leip Election Atlas
∆Republican vote share, 2000-2016/2020 Change in the vote share of the Republican party between the 2000 and 2016 (or 2020) Dave Leip Election Atlas
presidential elections.
Twitter data
Twitter users The number of Twitter users per county (in natural logarithm), based on tweets collected Gesis Datorium
using the Twitter streaming API in a 12 month period from June to November 2014 and
June to November 2015.
SXSW followers, March 2007 The number of Twitter users following the @SXSW account in each county that joined Twitter Search API
Twitter in March 2007.

8
SXSW followers, Pre The total number of Twitter users following the @SXSW account in each county that joined Twitter Search API
Twitter at any point in 2006.
County-level control variables
Geographical controls Include the distance (in miles) from the country centroid from Austin (TX), Chicago, NYC, U.S. Census Tigerline File
San Francisco, and Washington, DC, population density, and the logarithm of the land area
for each county.
Demographic controls Include the share of people in the age buckets 20-24, 25-29, 30-34, 40-44, 45-49 and 50+, as
well as the percentage change in county population between 2000 and 2016.
Socioeconomic controls Include a county’s poverty rate; unemployment rate; the share of the population employed U.S. Census/BLS/SimplyAnalytics
in construction/real estate, manufacturing, and information technology; the share of adults
over 25 with at least a high school degree or some college education; as well as prime time
TV viewership and the share of Fox News viewership.
China shock controls Include the measure of import competition from China, routine tasks, and offshorability from
Autor et al. (2013).
Election control Contains the vote share of the Republican party in the 1996 presidential election. Dave Leip Election Atlas
Table A.7: Search Terms Used to Create a
Proxy for Total Tweets

0 but his one these would

1 by how only they year
2 can if or think you
3 come in other this your
4 could into our time
5 day it out two
6 do its over up
7 even just people us
8 first know say use
9 for like see want
I from look she way
about get make so we
after give me some well
all go most take what
also good my than when
any have new that which
as he no their who
at he not them with
back her now then with
because him on there work
Notes: This table lists the 100 most common En-
glish words that were used as search terms to gen-
erate a proxy of “total tweets” used in Figure 4b.

9
A.1. Additional Details on the Logistic Regression Classifier
We train a seperate machine learning classifier for each of the three election years in our data
using the Python sci-kit package (Pedregosa et al., 2011). These classifiers help us to determine
whether tweets are more likely to be sent by Democratic-leaning or Republican-leaning users.
The classification process starts with the preparation of the underlying Twitter data. The
inputs are the text of each of the 4,300,579 tweets from U.S. Congress members. To focus on
election-related tweets, we restrict the sample to tweets that were sent either in the election
year or in the year leading up to the election (e.g. 2011 and 2012 for the 2012 election) and
mention at least one of the presidential candidates.
The target variable y for the classifier is an indicator variable equal to one for tweets
sent by Republicans and zero otherwise. The feature matrix X for the classifier are created by
count-vectorizing the texts of the tweets. In other words, we transform the text of the tweets
into n × v matrix, where n is the number of tweets and v is the number of unique 1,2-grams
that occur in the tweets. In preparation for this step, we removed common words (stopwords),
links, and special characters from the tweets. Additionally, we reduced the words in the
tweets to their morphological roots using a lemmatizer, which improves the performance of
the classifier. As an example, the lemmatizer changes words like “walking” and “walked” to
“walk”. Lastly, we reweight the n-grams v of tweet i using term frequency–inverse document
frequency (tf-idf):
1+T
tf idf (fi,v ) = (1 + ln(fi,v ) · (ln( ) + 1) (A.1)
1 + dv
where dv is the number of tweets n-gram v appears in. This reweighting reduces the importance
of words that appear frequently in many tweets, which help little to discriminate between
tweets. The tf-idf vectorizer also normalizes the feature matrix by its L2-norm.
The vector y and the matrix X then serve as the input for a L2 regularized logistic
regression classifier. The optimization of the classifier involves the minimization of the
following cost function54 :
n
X 1
min C log(exp(−yi (XiT w + c) + 1) + wT w (A.2)
w,c
i=1
2

where w are the weights (coefficients) of the logistic regression, c is a constant (intercept), and
C is the inverse of the regularization strength. Larger values of C imply weaker regularization.
For C −→ ∞, the classifier converges towards a normal logistic regression. As is standard in
most machine learning applications, we choose the optimal regularization strength C using
54
Note that this formulation of the cost function assumes that yi takes values −1; 1. We use this formulation
in line with the sci-kit documentation.

10
10-fold cross-validation. This involves randomly splitting the training data into ten equal
slices. Nine of the ten slices are then used to train the classifier, while the out-of-sample
performance is evaluated against the remaining slice using F1-scores.
The final classifiers achieves an out-of-sample F1-score of 0.916 in 2012, of 0.843 in
2016 and of 0.904 in 2020. The classifiers, therefore, accurately predict the party affiliation
of Congress members. We then take these classifiers and apply it to the universe of tweets
sent during the 2012, 2016 and 2020 presidential election. For each tweet in the election
data, the classifiers provides us with a predicted class (either Democrat or Republican) and
a probability for this class label. To avoid that our results are driven by tweets for which
the classifier is “uncertain,” we code tweets with a predicted class probability below 60% as
neutral. This adjustment has no bearing on our findings. In spirit, this approach is similar
to the work of Gentzkow and Shapiro (2010). While they identify expressions that are more
frequently used by Democrats and Republicans by hand, we use a machine learning classifier
to identify n-grams in the tweets of Congress members that help us to differentiate between
the two parties.
We visualize the most predictive n-grams identified by the classifiers for each election
cycle in Figure A.4. Overall, the classifiers identifies words, hashtags, and Twitter handles
that are intuitively associated with a Republican slant for each election year. Among the
most predictive term are the hashtags “tcot” (Top Conservatives on Twitter) and “maga”
(Make America great again) and particularly in 2020 many references to Donald Trump’s
Twitter account (“realdonaldtrump”).

11
Figure A.4: Most Predictive Terms Of “Republican” Tweets by Election
(a) 2012 Election

(b) 2016 Election

(c) 2020 Election

Notes: This word cloud plots the n-grams most predictive of tweets sounding like those of Republican
Congress members, as identified by the logistic regression classifier for each election cycle. The size of
the word represents the magnitude of the coefficients.
12
B Additional Details on the SXSW Festival

Figure B.1: Screenshot Quote from Twitter Founder

Notes: This screenshot shows the full post of Twitter co-founder Evan Williams posted on Quora
on January 4, 2011 describing the role of the SXSW festival in the platform’s rise to popularity
(Quora, 2011).

13
 Figure B.2: Number of SXSW Follower Joining each Month

Number of followers joining Twitter 1,500

1,000

500

0
06

06

06

em 06

cto 06

em 6

em 06

nu 6

M 07
07
0
y0

ov er 0

r0

Fe ry 0
ch

ril

ay

ne

D ber

ry

ch
s
be

be
l
Ju

gu

a
ua
Ap

M
ar

Ju

ar
Au

br
M

Ja
pt

ec
O
Se

Notes: The figures shows the number of SXSW Follower joining in each month.

Table B.1: Balancedness of SXSW Counties’ User Characteristics

First names (Corr. = 0.63) Terms used in bio (Corr. = 0.89)

Pre-period March 2007 Pre-period March 2007
michael michael http http
paul john founder com
mike chris com digital
chris jeff tech founder
eric matt product medium
justin brian co director
ryan david digital tech
kevin alex director music
jeff jason design social
david kevin social marketing
Notes: This table presents the ranking of the most common first names and
terms used in a Twitter user’s “bio” among users who follow “South by South-
west” on Twitter, depending on whether they joined during March 2007 or in
the pre-period.

14
Figure B.3: Additional Evidence for the Impact of the SXSW Festival
(a) Long-term Effects of the 2007 SXSW on Twitter
Adoption

Diff-in-diff estimate of SXSW on Twitter usage

0.006 SXSW festival starts

0.004

0.002

0.000
2006q2 2009q1 2012q1 2015q1

(b) Connections to the SXSW festival

0.6
Share of Twitter users with SXSW connection

0.4

0.2

0.0
2007q1 2009q1 2011q1 2013q1 2015q1

Notes: The figures provide evidence on the long-term impact of the SXSW festival on Twitter
usage across
P the United States. Panel (a) plots P the βτ from the panel event study regression
usersct = τ βτ SXSWcM arch2007 × 1(t = τ ) + τ δτ SXSWcP re × 1(t = τ ) + θc + γt + εct where
usersct is the number of Twitter users per capita in county c on quarter t, SXSWcM arch2007 is the
logarithm of (one plus) the number of SXSW followers in county c who joined Twitter in March
2007 and SXSWcP re is a similarly defined variable for followers who joined Twitter before March
2007. We standardize the variables to have a mean of zero and standard deviation of one. The
whiskers represent 95% confidence intervals based on standard errors clustered by state, where
2006q4 serves as excluded period. While the confidence intervals for 2006q2 and 2006q3 cannot be
seen, they include zero. Panel (b) plots the share of Twitter who either follow SXSW or follow an
user that follows SXSW.

15
Table B.2: Are Twitter Users in Counties With SXSW Followers Different?

User first names Terms used in user bio

(Corr. = 0.97) (Corr. = 0.94)
Other counties SXSW counties Other counties SXSW counties
michael michael love co
chris david life love
john chris co life
david john http http
sarah alex http co http co
mike mike god music
emily matt ig lover
ryan sarah music ig
matt ryan university de
alex andrew like like
Notes: This table compares the individual characteristics of Twitter users
from counties with “South by Southwest” followers who joined in March
2007 (“SXSW counties”) to Twitter users from all other U.S. counties
(“Other counties”). We plot the ranking of the most common first names
and terms used in a Twitter user’s “bio”.

16
C Additional Robustness Checks

Table C.1: LASSO Variable Selection for 2016/2020 Election

Dep. var.: Republican vote share in...

Census Region FE State FE
Controls Controls2 Controls Controls2
(1) (2) (3) (4)
Panel A: 2SLS 2016 Election
Log(Twitter users) -0.030** -0.039*** -0.031*** -0.046***
(0.012) (0.013) (0.012) (0.013)
Observations 3,064 3,064 3,064 3,064
Nr. Controls 48 609 93 654
Nr. selected controls 30 70 50 87
Panel B: 2SLS 2020 Election
Log(Twitter users) -0.030** -0.035*** -0.029** -0.039***
(0.012) (0.013) (0.013) (0.013)
Observations 3,064 3,064 3,064 3,064
Nr. Controls 48 609 93 654
Nr. selected controls 30 73 51 91
Notes: This table presents county-level regressions where the dependent
variable is the Republican vote share in the 2016 or 2020 presidential
election. Log(Twitter users) is instrumented using the number of users
who started following SXSW in March 2007 (in logs with 1 added inside).
Columns 1 includes census region fixed effects and allows all potential
control variables discussed in the text (48 controls) to be selected by the
LASSO procedure. Columns 2 includes census region fixed effects and
allows for interactions of all control variables with each other (609 poten-
tial controls). Column 3 includes state fixed effects and allows all control
variables to be selected (93 potential controls). Column 4 includes state
fixed effects and allows all interactions of control variables with each
other to be selected (654 potential controls). In all regressions the in-
cluded controls are selected using the partialing-out LASSO procedure
from Chernozhukov et al. (2015a,b). Standard errors in parentheses are
clustered at the state level. *** p < 0.01, ** p < 0.05, * p < 0.1.

17
Figure C.1: Twitter and the Republican Vote Share, 1924-1996 (Reduced Form)

1924

1928

1932

1936

1940

1944

1948

1952

1956

1960

1964

1968

1972

1976

1980

1984

1988

1992

1996

-.04 -.02 0 .02 .04

Point estimate on Log(SXSW followers, March 2007)

Notes: This figure plots reduced form estimates β̂ 0 from county-level regressions as in equation
(2). These estimates reflect the correlation of Log(1 + SXSW followers, March 2007) with the
Republican vote share in presidential elections while controlling for Log(1 + SXSW followers, Pre).
All regressions control for population deciles and Census region fixed effects, and the full set of
controls except 1996 Election controls (same as columns 4 and 9 of Table 2). Regressions are
weighted by turnout in the 2000 presidential election. Whiskers represent 95% confidence intervals
based on standard errors clustered by state.
18
 Table C.2: Placebo Tests for Other Festivals

Twitter Followers of Festival joining in Festival Month

SXSW Burning Man Coachella Pitchfork EDC ACL
(1) (2) (3) (4) (5) (6)
Panel A: First Stage (Dep. Var.: Twitter Usage)
Followers Festival Month 0.167*** -0.005 0.011 -0.006 0.010 -0.009
(0.015) (0.012) (0.012) (0.006) (0.008) (0.018)
Panel B: Reduced form (Dep. Var.: Rep. Vote Share 2016)
Followers Festival Month -0.004*** 0.001 -0.000 -0.000 0.000 -0.000
(0.001) (0.001) (0.001) (0.000) (0.001) (0.001)
Panel C: Reduced form (Dep. Var.: Rep. Vote Share 2020)
Followers Festival Month -0.003** 0.000 -0.001 -0.000 0.000 -0.000
(0.001) (0.001) (0.001) (0.000) (0.001) (0.001)
Census region FE Yes Yes Yes Yes Yes Yes
Population controls Yes Yes Yes Yes Yes Yes
Geographical controls Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes
Socioeconomic controls Yes Yes Yes Yes Yes Yes
China shock controls Yes Yes Yes Yes Yes Yes
1996 election control Yes Yes Yes Yes Yes Yes
Observations 3,064 3,064 3,064 3,064 3,064 3,064
Notes: This table presents the first stage and reduced from estimates using equation (3). The depen-
dent variable is either Twitter usage (Panel A) or the vote share of the Republican party in the 2016
and 2020 presidential elections (Panels B and C). The independent variables are number of followers
of different festivals that joined in the respective festival month (in logs with 1 added inside). To
make the coefficients comparable, we standardized these variables to have mean zero and standard
deviation one; this means the results for SXSW are not directly comparable in magnitude to those
in the main text. Similarly to our baseline specification, all regressions control for the number of
festival followers that joined Twitter before the festival. As the festivals take place after the SXSW
festival, we additionally control for the number of SXSW followers from March 2007. All regressions
are weighted by turnout in the 2000 presidential election. All regressions include the controls from
columns 5 and 10 in Table 2. EDC is the Electric Daisy Carnival and ACL is Austin City Limits.
Standard errors in parentheses are clustered by state. *** p < 0.01, ** p < 0.05, * p < 0.1.

19
 Table C.3: Twitter and the Republican Vote Share – Robustness

No Election Pre-period Pre-period No zero Spatial Per Capita

regression year control control SXSW user standard Twitter
weights weights polynomial deciles counties errors Usage
(1) (2) (3) (4) (5) (6) (7)
Panel A: Republican vote share in 2016
Log(Twitter users) -0.037*** -0.021** -0.019*** -0.020*** -0.030** -0.037***
(0.011) (0.008) (0.007) (0.007) (0.012) (0.011)
Log(Twitter users p.c.) -0.091**
(0.044)
Observations 3,064 3,064 3,064 3,064 165 3,064 3,064
Mean of DV 0.64 0.46 0.46 0.46 0.34 0.64 0.46
Robust F-stat. 72.94 94.50 125.40 114.91 23.46 54.13 24.50
Panel B: Republican vote share in 2020
Log(Twitter users) -0.036** -0.020** -0.018** -0.019** -0.033** -0.036***
(0.014) (0.010) (0.008) (0.009) (0.016) (0.012)
Log(Twitter users p.c.) -0.086*
(0.047)
Observations 3,064 3,064 3,064 3,064 165 3,064 3,064
Mean of DV 0.65 0.47 0.47 0.47 0.35 0.65 0.47
Robust F-stat. 72.94 88.47 125.40 114.91 23.46 54.13 24.50
Notes: This table presents 2SLS results estimated using equation (3). The dependent variable is the vote share of
the Republican party in the 2016 and 2020 presidential elections in panel A and B, respectively. Log(Twitter users)
is instrumented using the number of users who started following SXSW in March 2007 (in logs with 1 added inside).
All regressions except columns 1, 2, and 6 are weighted by turnout in the 2000 presidential election. Column 1
omits regression weights. Column 2 weights by the turnout in the election year (2016 or 2020) instead of 2000.
Columns 3 and 4 control for a fifth-order polynomial and deciles of SXSW followers who joined Twitter before the
SXSW 2007 event, respectively. Column 5 drops all counties that had no SXSW followers joining Twitter in March
2007 or in the period before. Column 6 uses spatial standard errors based on the method proposed in Colella et al.
(2019), implemented in Stata as acreg, using a 200 miles cutoff. Column 7 uses the number of Twitter users per
capita (in logs with 1 added inside). All regressions include the controls from columns 5 and 10 in Table 2. In
columns 1 to 5, 7, and 8, standard errors in parentheses are clustered by state. *** p < 0.01, ** p < 0.05, * p < 0.1.

20
Table C.4: Twitter and Changes in Republican Vote Share, 2004-2020

Dep. var.: ∆Republican vote share between...

2000-04 2000-08 2000-12 2000-16 2000-20
(1) (2) (3) (4) (5)
Panel A: Reduced form
Log(SXSW followers, March 2007) -0.002 -0.003 -0.001 -0.009** -0.008**
(0.002) (0.003) (0.003) (0.004) (0.004)
Log(SXSW followers, Pre) -0.000 -0.001 -0.002 0.000 -0.002
(0.003) (0.005) (0.003) (0.004) (0.005)
Panel B: 2SLS
Log(Twitter users) -0.004 -0.006 -0.002 -0.017** -0.015**
(0.004) (0.006) (0.006) (0.007) (0.007)
Log(SXSW followers, Pre) 0.000 -0.001 -0.002 0.001 -0.001
(0.003) (0.005) (0.003) (0.005) (0.005)
Observations 3,064 3,064 3,064 3,064 3,064
Mean of DV 0.03 -0.02 -0.01 -0.02 -0.01
Robust F-stat. 121.18 121.18 121.18 121.18 121.18
Notes: This table presents county-level regressions where the dependent variable is the
change in the vote share of the Republican party between 2000 and the indicated year.
Log(SXSW followers, March 2007) is the number of Twitter users (in logs, with 1 added
inside) who joined in March 2007 and follow South by Southwest (SXSW). SXSW followers,
Pre is the number of SXSW followers who registered at some point in 2006, defined similarly.
Twitter users are the number of users in 2014-2015. All regressions control for population
deciles, Census region fixed effects, and the full set of controls (as in columns 5 and 10 of
Table 2). The first-stage regressions for 2SLS results (Panel B) are presented in Table 1,
with the F-stat for the excluded instrument in the bottom row. On Panel (a), the coeffi-
cient on Log(SXSW followers, March 2007) for 2004, 2008, and 2012 are jointly statistically
insignificant (p-value=0.398). Further, the average effect in 2016 and 2020 is statistically
distinct from the average effect in 2004, 2008, and 2012 (p-value=0.001). Observations are
weighted by turnout in the 2000 presidential election. Standard errors in parentheses are
clustered by state. *** p < 0.01, ** p < 0.05, * p < 0.1.

21
Table C.5: Time-Varying Twitter Usage and Changes in Vote Shares

Dep. var.: ∆Republican vote share between...

2000-04 2000-08 2000-12 2000-16 2000-20
(1) (2) (3) (4) (5)
Panel A: First stage
Log(SXSW followers, March 2007) 0.644*** 0.644*** 0.546*** 0.523*** 0.523***
(0.055) (0.055) (0.047) (0.048) (0.048)
Panel B: Reduced form
Log(SXSW followers, March 2007) -0.002 -0.003 -0.001 -0.009** -0.008**
(0.002) (0.003) (0.003) (0.004) (0.004)
Log(SXSW followers, Pre) -0.000 -0.001 -0.002 0.000 -0.002
(0.003) (0.005) (0.003) (0.004) (0.005)
Panel C: 2SLS
Log(Twitter users) -0.003 -0.005 -0.002 -0.017** -0.015**
(0.003) (0.005) (0.005) (0.007) (0.007)
Log(SXSW followers, Pre) 0.000 -0.001 -0.002 0.001 -0.001
(0.003) (0.005) (0.003) (0.005) (0.005)
Observations 3064 3064 3064 3064 3064
Mean of DV 0.026 -0.024 -0.008 -0.018 -0.009
Robust F-stat. 135.88 135.88 134.88 121.18 121.18
Twitter usage measured in 2008 2008 2012 2016 2016
Notes: This table presents county-level regressions where the dependent variable is the change
in the vote share of the Republican party between 2000 and the indicated year (except for Panel
B, where the dependent variable is Twitter users. Log(SXSW followers, March 2007) is the
number of Twitter users (in logs, with 1 added inside) who joined in March 2007 and follow
South by Southwest (SXSW). SXSW followers, Pre is the number of SXSW followers who
registered at some point in 2006, defined similarly. Differently from other tables, Twitter users
varies over time, as opposed to being fixed to 2014-2015. All regressions control for population
deciles, Census region fixed effects, and the full set of controls (as in columns 5 and 10 of Table
2). Observations are weighted by turnout in the 2000 presidential election. Standard errors in
parentheses are clustered by state. *** p < 0.01, ** p < 0.05, * p < 0.1.

22
 Table C.6: Twitter and the Ross Perot Vote

Dep. var.: Vote share Ross Perot in...

1992 1996
(1) (2) (3) (4)
Panel A: Reduced form
Log(SXSW followers, March 2007) 0.000 -0.000 0.003 0.003
(0.002) (0.002) (0.003) (0.003)
Panel B: 2SLS
Log(Twitter users) 0.000 -0.001 0.006 0.007
(0.003) (0.003) (0.006) (0.006)
Population deciles Yes Yes Yes Yes
Census region FE Yes Yes Yes Yes
Geographical controls Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes
Socioeconomic controls Yes Yes Yes Yes
China shock controls Yes Yes Yes Yes
1996 election control Yes Yes
Observations 3,064 3,064 3,064 3,064
Mean of DV 0.10 0.10 0.20 0.20
Robust F-stat. 118.21 121.18 118.21 121.18
Notes: This table presents county-level regressions where the dependent variable
is the third party vote share in the 1992 or 1996 presidential election. Log(SXSW
followers, March 2007) is the number of Twitter users (in logs, with 1 added in-
side) who joined in March 2007 and follow South by Southwest (SXSW). SXSW
followers, Pre is the number of SXSW followers who registered at some point
in 2006, defined similarly. Twitter users are the number of users in 2014-2015.
The first-stage regressions for 2SLS results (Panel B) are presented in Table 1,
with the F-stat for the excluded instrument in the bottom row. Observations
are weighted by turnout in the 2000 presidential election. Standard errors in
parentheses are clustered by state. *** p < 0.01, ** p < 0.05, * p < 0.1.

23
 Table C.7: Twitter and Vote Shares in Democratic Primaries

Dep. var.: Vote share in Democratic Primary of...

Clinton Sanders Warren Biden Sanders Buttigieg Bloomberg Klobuchar
2016 2016 2020 2020 2020 2020 2020 2020
(1) (2) (3) (4) (5) (6) (7) (8)
Panel A: Reduced form
Log(SXSW followers, March 2007) 0.005 -0.004 0.002 -0.009 0.017*** -0.003 0.001 -0.003
(0.010) (0.010) (0.006) (0.014) (0.006) (0.002) (0.004) (0.002)
Panel B: 2SLS
Log(Twitter users) 0.008 -0.006 0.003 -0.014 0.025** -0.004 0.001 -0.004
(0.014) (0.014) (0.009) (0.021) (0.010) (0.003) (0.006) (0.002)
Observations 2,656 2,656 2,769 2,769 2,769 2,769 2,769 2,769
Mean of DV 0.55 0.43 0.06 0.56 0.24 0.02 0.06 0.01
Robust F-stat. 67.94 67.94 73.68 73.68 73.68 73.68 73.68 73.68
Notes: This table presents county-level regressions where the dependent variable is the vote share of the indicated candidate in the
Democratic party primaries in 2016 or 2020. Log(SXSW followers, March 2007) is the number of Twitter users (in logs, with 1 added
inside) who joined in March 2007 and follow South by Southwest (SXSW). SXSW followers, Pre is the number of SXSW followers
who registered at some point in 2006, defined similarly. Twitter users are the number of users in 2014-2015. All regressions control
for population deciles, Census region fixed effects, and the full set of controls (as in columns 5 and 10 of Table 2). The first-stage
regressions for 2SLS results (Panel B) are analogous to the one presented in Table 1, except for the different sample of counties for
which primary results are available. The F-stat for the excluded instrument is provided in the bottom row. Observations are weighted
by turnout in the 2000 presidential election. Standard errors in parentheses are clustered by state. *** p < 0.01, ** p < 0.05, *
p < 0.1.

Table C.8: Twitter and Vote Decisions in the 2016 CCES – Robustness

Dep. var.: Voted for Trump in 2016

(1) (2) (3) (4) (5)
Intended Intended
Verified Vote Trump other
Baseline vote intention vote vote
Log(Twitter users) -0.135*** -0.154*** -0.133** -0.230*** -0.064*
(0.045) (0.054) (0.052) (0.082) (0.034)
Marginal effect [-0.049] [-0.055] [-0.048] [-0.005] [-0.013]
Observations 146,579 56,375 46,418 14,723 24,354
Mean of DV 0.492 0.497 0.455 0.991 0.137
Notes: This table presents results estimated using IV probit models, as in equation
(4). The dependent variable is a dummy for individuals in the CCES who voted
for Trump in 2016. Log(Twitter users) is instrumented using the number of SXSW
followers that joined Twitter in March 2007. All regressions control for the (log)
number of SXSW followers that joined Twitter at some point in 2006, family income,
gender, education levels, marital status, news interest, and age, as well as county-
level population deciles and Census region fixed effects. Regressions are weighted by
survey weights. Standard errors in parentheses are clustered by state. *** p < 0.01,
** p < 0.05, * p < 0.10.

24
D Further Results

Table D.1: Additional Outcomes

Switching prob. ∆Campaign don., 2000-16 Trump approval, 2017

Obama to Trump Democrats Republicans Democrats Republicans
(1) (2) (3) (4) (5)
Log(Twitter users) -0.138*** 0.866*** 0.168 -0.011** -0.037***
(0.048) (0.185) (0.235) (0.005) (0.009)
Observations 3,065 2,250 2,446 2,727 2,920
Mean of DV 0.105 1.943 1.096 0.066 0.850
Robust F-stat. 74.65 62.57 64.34 60.20 66.44
Notes: This table presents results from county-level regressions of equation (3). Column 1 shows results
from an IV probit regression where the dependent variable is a dummy equal to 1 for the 217 counties for
which both Obama and Trump gained the majority of votes in 2008 and 2016, respectively. In columns
2 and 3, the dependent variable is the difference in the natural logarithm of campaign donations to the
Democratic and Republican party, respectively, between 2000 and 2016. In columns 4 and 5, the dependent
variable is the share of respondents in the Gallup Daily Poll approving of Trump in 2017. Log(Twitter
users) is instrumented using the number of users who started following SXSW in March 2007. All regres-
sions control for population deciles and Census region fixed effects and geographical controls. Regressions
are weighted by turnout in the 2000 presidential election. Standard errors in parentheses are clustered by
state. *** p < 0.01, ** p < 0.05, * p < 0.1.

25
 Table D.2: Twitter and Changes in Voter Turnout, 2004-2020

∆Votes cast/voting age pop.

2000-04 2000-08 2000-12 2000-16 2000-20
(1) (2) (3) (4) (5)
Panel A: Reduced form
Log(SXSW followers, March 2007) -0.000 -0.000 -0.001 0.001 0.007**
(0.003) (0.003) (0.004) (0.004) (0.003)
Log(SXSW followers, Pre) -0.000 0.000 -0.002 -0.001 -0.005
(0.006) (0.004) (0.005) (0.005) (0.005)
Panel B: 2SLS
Log(Twitter users) -0.000 -0.000 -0.001 0.002 0.014**
(0.006) (0.006) (0.008) (0.008) (0.006)
Log(SXSW followers, Pre) -0.000 0.000 -0.002 -0.001 -0.006
(0.006) (0.005) (0.005) (0.005) (0.005)
Observations 3,063 3,063 3,063 3,063 3,063
Mean of DV 0.088 0.079 0.053 0.057 0.126
Robust F-stat. 121.23 121.23 121.23 121.23 121.23
Notes: This table presents county-level regressions where the dependent variable is the
change in the voter turnout (as a share of voting age population) between 2000 and the
indicated year. Log(SXSW followers, March 2007) is the number of Twitter users (in logs,
with 1 added inside) who joined in March 2007 and follow South by Southwest (SXSW).
SXSW followers, Pre is the number of SXSW followers who registered at some point in
2006, defined similarly. Twitter users are the number of users in 2014-2015. All regressions
control for population deciles, Census region fixed effects, and the full set of controls (as
in columns 5 and 10 of Table 2). The first-stage regressions for 2SLS results (Panel B)
are presented in Table 1, with the F-stat for the excluded instrument in the bottom row.
Observations are weighted by turnout in the 2000 presidential election. Standard errors in
parentheses are clustered by state. *** p < 0.01, ** p < 0.05, * p < 0.1.

26
 Table D.3: Twitter and Congressional Elections – 2SLS Estimates

Panel A: House elections

∆Republican vote share in House election between...
2000-02 2000-04 2000-06 2000-08 2000-10 2000-12 2000-14 2000-16 2000-18 2000-20
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Log(Twitter users) 0.014 -0.003 0.027 0.032 0.019 0.023 0.039 0.006 0.028 0.023
(0.018) (0.019) (0.020) (0.022) (0.021) (0.023) (0.024) (0.024) (0.023) (0.023)
Observations 2,982 2,955 2,971 2,957 2,969 2,983 2,978 2,980 2,983 2,982
Mean of DV 0.02 0.01 -0.04 -0.06 0.03 -0.01 0.02 0.00 -0.04 -0.01
Robust F-stat. 109.63 110.42 109.20 109.49 109.83 109.68 110.07 109.87 109.68 109.53

Panel B: Senate elections

∆Republican vote share in Senate election between...

27
1996-02 1998-04 2000-06 1996-08 1998-10 2000-12 1996-14 1998-16 2000-18 2000-20
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Log(Twitter users) 0.019 0.015 0.007 0.008 -0.011 0.021 -0.035 -0.013 -0.008 -0.019
(0.023) (0.024) (0.021) (0.021) (0.018) (0.031) (0.026) (0.026) (0.022) (0.018)
Observations 2,183 1,985 1,832 2,183 1,985 1,832 2,183 1,985 1,832 2,183
Mean of DV 0.01 -0.03 -0.06 -0.05 0.01 -0.06 0.03 -0.07 -0.10 -0.00
Robust F-stat. 79.24 85.91 72.85 79.24 85.91 72.85 79.24 85.91 72.85 79.24
Notes: This table presents results estimated using 2SLS, as in equation (3). For House elections in Panel A, the dependent
variable is the change in the Republican vote share since 2000. For Senate elections in Panel B, the dependent variable is
the change in the Republican vote share from six, twelve, or eighteen years ago (to accommodate senators’ 6-year terms).
Log(Twitter users) is instrumented using the number of users who started following SXSW in March 2007 (in logs with 1 added
inside). All regressions control for the (log) number of SXSW followers that joined Twitter at some point in 2006, population
deciles and Census region fixed effects and the full set of controls (as in columns 5 and 10 of Table 2). Regressions are weighted
by turnout in the 2000 presidential election. Standard errors in parentheses are clustered by state. *** p < 0.01, ** p < 0.05, *
p < 0.1.
 Table D.4: Twitter and Individuals’ Vote Decisions in 2016 (Age Splits)

Dep. var.: Voted for Trump in 2016

Full Strong Mod. Mod. Strong
Sample Dem. Dem. Indep. Rep. Rep.
(1) (2) (3) (4) (5) (6)
Panel A: Age Group 18-49
Log(Twitter users) -0.178*** 0.008 -0.164** -0.220*** -0.069 -0.014
(0.053) (0.089) (0.078) (0.063) (0.078) (0.082)
Marginal effect [-0.062] [0.001] [-0.025] [-0.079] [-0.013] [-0.001]
Observations 57,013 18,624 15,108 5,110 8,794 8,275
Mean of DV 0.410 0.040 0.092 0.509 0.879 0.961
Panel B: Age Group 50+
Log(Twitter users) -0.129** -0.022 -0.041 -0.225** 0.015 0.113
(0.052) (0.083) (0.106) (0.097) (0.078) (0.081)
Marginal effect [-0.047] [-0.001] [-0.008] [-0.075] [0.002] [0.003]
Observations 89,324 25,548 15,577 8,485 18,955 20,158
Mean of DV 0.546 0.017 0.130 0.680 0.939 0.988
Notes: This table presents results estimated using IV probit models, as in equation (4). The dependent
variable is a dummy for individuals in the CCES who voted for Trump in 2016. Log(Twitter users) is
instrumented using the (log) number of SXSW followers that joined Twitter in March 2007. All regres-
sions control for the (log) number of SXSW followers that joined Twitter at some point in 2006, family
income, gender, education levels, marital status, news interest, and age, as well as county-level popula-
tion deciles and Census region fixed effects. Regressions are weighted by survey weights. Standard
errors in parentheses are clustered by state. *** p < 0.01, ** p < 0.05, * p < 0.10.

28
Table D.5: Twitter and the Republican Vote Share in Swing and Safe Counties

Swing Republican Democratic Safe

counties counties counties counties
(1) (2) (3) (4)
Panel A: ∆Republican vote share 2000-2016
Log(Twitter users) -0.073*** -0.006 -0.008 -0.005
(0.024) (0.008) (0.008) (0.006)
Log(SXSW followers, Pre) 0.013 -0.001 0.000 -0.002
(0.015) (0.014) (0.008) (0.005)
Observations 716 1,990 358 2,348
Mean of DV -0.033 0.021 -0.040 -0.012
Robust F-stat. 14.70 11.97 105.57 99.87
Panel B: ∆Republican vote share 2000-2020
Log(Twitter users) -0.066*** -0.017** -0.013 -0.007
(0.019) (0.007) (0.009) (0.006)
Log(SXSW followers, Pre) 0.006 -0.005 -0.001 -0.003
(0.012) (0.013) (0.009) (0.006)
Observations 716 1,990 358 2,348
Mean of DV -0.027 0.026 -0.026 -0.002
Robust F-stat. 14.70 11.97 105.57 99.87
Notes: This table presents results estimated using 2SLS, as in equation (3). The
dependent variable is the change in the vote share of the Republican party between
the 2000 and 2016/2020 presidential elections in Panels A and B, respectively.
Swing counties are those that were not consistently won by either Republicans
or Democrats between 2000 and 2012; Republican and Democratic counties are
those who voted consistently. Safe counties are the counties from columns (2) and
(3) combined. Log(Twitter users) is instrumented using the number of users who
started following SXSW in March 2007. SXSW followers, Pre is the number of
SXSW followers who registered at some point in 2006. All regressions control for
population deciles and Census region fixed effects and the full set of controls (as
in columns 5 and 10 of Table 2). Regressions are weighted by turnout in the 2000
presidential election. Standard errors in parentheses are clustered by state. ***
p < 0.01, ** p < 0.05, * p < 0.1.

29
 Figure D.1: Twitter’s Partisan Slant (Tweet Measure)
(a) Tweets about Republican Presiden- (b) Tweets about Democratic Presiden-
tial Candidates tial Candidates
34%
28%
250 37% 250
Number of Tweets (in millions)

Number of Tweets (in millions)

200 200

150 150

24% 31%
100 27% 100 27%
49% 42%
22%
50 35% 50 38% 39%
14% 36% 39%
51% 36%
0 0
Election 2012 Election 2016 Election 2020 Election 2012 Election 2016 Election 2020

Democratic Slant Republican Slant Neutral Slant Democratic Slant Republican Slant Neutral Slant

Notes: These figures present the number of tweets (as opposed to the number of “likes” of such tweets in
Figure 9) that contain the last name of the candidates in the 2012, 2016 and 2020 presidential elections,
depending on whether the tweet was classified as having a Republican (instead of Democratic) slant.
We classify the slant of a tweet based on the Twitter network of the user who sent the tweet. If the user
follows more Democratic than Republican Congress members, they will be classified as a Democrat, and
vice versa. Users who follow an equal number of Democrats and Republican or no Congress members
are classified as neutral.

30
 Figure D.2: Twitter’s Partisan Slant (Text-Based Classifier)
(a) Likes for Tweets about Republican (b) Likes for Tweets about Democratic
Presidential Candidates Presidential Candidates
11%
600
38% 600
51%
Number of Likes (in millions)

Number of Likes (in millions)

500 500

400 400

300 300 10%

47%
200 200 43%
17%
39%
13%
100 9% 44% 100 12%
65%
12% 36%
22%
0
79% 0
52%
Election 2012 Election 2016 Election 2020 Election 2012 Election 2016 Election 2020

Democratic Slant Republican Slant Neutral Slant Democratic Slant Republican Slant Neutral Slant

(c) Tweets about Republican Presiden- (d) Tweets about Democratic Presiden-
tial Candidates tial Candidates
12%
53%
250 35% 250
Number of Tweets (in millions)

Number of Tweets (in millions)

200 200

150 150

15% 7%
100 30% 100 47%
55% 46%
11%
50 9% 50 14% 51%
12% 40% 38%
78% 46%
0 0
Election 2012 Election 2016 Election 2020 Election 2012 Election 2016 Election 2020

Democratic Slant Republican Slant Neutral Slant Democratic Slant Republican Slant Neutral Slant

Notes: These figures present the number of “likes” received by tweets, or the number of tweets, that
contain the last name of the candidates in the 2012, 2016 and 2020 presidential elections, depending on
whether the tweet was classified as having a Republican (instead of Democratic) slant. We classify the
slant of a tweet based on similarity in the language to that of a congressional Republican or Democrat,
using a L2 regularized logistic regression classifier using the tweets sent by Congress members. Optimal
normalization strength is chosen using 10-fold cross-validation. Tweets with a predicted class probability
below 60% are coded as neutral. See Appendix A.1. for details.

31
 Figure D.3: Specification Curve
(a) 2016 Presidential Election Results
0
-.02
Coefficient Twitter Usage (IV)

-.04
-.06
-.08
-.1

Census_Region
Socioeconomic
Demographic
China_Shock
Geographic
Population
Election
Ethnic
TV
-.12

Population
Census_Region
Geographic
pad

Demographic
Ethnic
Socioeconomic
TV
China_Shock
Census_Region
Socioeconomic
Demographic
China_Shock
Geographic
Population
Election
Ethnic
TV

Notes: These figures plot the 2SLS estimates and 95% confidence intervals from a regression of the
Republican vote share in 2016 on Log(T witter users), instrumented with SXSW M arch 2007 . All
regressions include population deciles, census region fixed effects, and SXSW P re . The combination
of the other included control variables is shown at the bottom; filled circles mean a set of controls
was included. The baseline specification with all controls is marked by the vertical line.

32
 Figure D.3: Specification Curve
(b) 2020 Presidential Election Results
0
-.02
Coefficient Twitter Usage (IV)

-.04
-.06
-.08
-.1

Census_Region
Socioeconomic
Demographic
China_Shock
Geographic
Population
Election
Ethnic
TV

Population
Census_Region
Geographic
pad

Demographic
Ethnic
Socioeconomic
TV
China_Shock
Census_Region
Socioeconomic
Demographic
China_Shock
Geographic
Population
Election
Ethnic
TV

Notes: These figures plot the 2SLS estimates and 95% confidence intervals from a regression of the
Republican vote share in 2020 on Log(T witter users), instrumented with SXSW M arch 2007 . All
regressions include population deciles, census region fixed effects, and SXSW P re . The combination
of the other included control variables is shown at the bottom; filled circles mean a set of controls
was included. The baseline specification with all controls is marked by the vertical line.

33
E Additional Details on the Extrapolation for the Av-
erage Treatment Effect
Andrews and Oster (2019) show how selection into participating in an experiment can be
used to make extrapolations regarding the external validity of an experiment. They illustrate
their approach using the experiment from Bloom et al. (2015) within a Chinese call centre
in which workers were asked to volunteer for a work-from-home program. 50% of workers
volunteered and were then randomly assigned to either treatment and control group. Given
this, the measured treatment effect from the experimental sample might be different from the
average treatment effect for the population as a whole, since the volunteers likely receive a
higher utility from the work-from-home program.
When a set of covariates X is observed for both the “experimental sample” and “popu-
lation,” Andrews and Oster (2019) provide a procedure that uses effect heterogeneity based
on X estimated within the experimental sample to extrapolate to the average treatment effect
for the “population.”
We build on their procedure and argue that we can similarly use heterogeneity in the
treatment effect within the counties that “identify” our results to extrapolate the treatment
effect to all other counties in the US. Column (5) of Table C.3 show that we obtain similar
estimates to our baseline when we only compare counties with SXSW followers that joined
Twitter in March 2007 to counties with followers that joined in the pre-period, while excluding
those counties in neither group. We can use this subsample of counties as the “experimental
sample,” and extrapolate effects to the “population” of all other counties.
Since Andrews and Oster (2019) approach is designed for a binary treatment, we adjust
our regression framework by defining a treatment indicator variable equal to 1 for counties
with SXSW followers who joined in March 2007 and 0 for the counties with followers that
joined in the pre-period. We estimate the treatment effect for the subsample of counties
that do not have zero SXSW followers in both periods using the regression specification
yc = α + β · 1[SXSWcM arch2007 > 0] + c . The resulting treatment effect estimate is −0.075,
which is similar Table 2 Panel B column (1).55 We then perform a linear prediction of this
treatment effect based all observable variables in Table A.5 within this subsample. The
resulting predicted treatment effect is −0.085. Last, we extrapolate the treatment effect for
the rest of US counties. Based on the variation in observable characteristics we would predict
an ATE of −0.218 for the US overall.
55
Note that our regression specification does not include controls, as in the Andrews and Oster (2019)
approach. Moreover, partialling out the controls and applying the Frisch-Waugh theorem was not feasible
since the residualized treatment would no longer be binary.

34
 Note that this extrapolation is based on adjusting our reduced-form estimates to use a
binary indicator variable for treatment thus the coefficients are not directly comparable to
our baseline estimates. The approach further assumes quasi-random treatment assignment
with in the counties with SXSW variation. Taken this into account, the extrapolation should
therefore be viewed as suggestive, but confirming the notion that the effect for all US counties
would be larger since the more urban counties for which we have variation in our instrument
tend to be Democratic strongholds, and thus likely have fewer independents and moderate
Republicans, for which we find the largest persuasion effects (in survey data).

35