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Analyzing Social Media Activities at Bellingcat

Published: 30 April 2023 Publication History

Abstract

Open-source journalism emerged as a new phenomenon in the media ecosystem, which uses crowdsourcing to fact-check and generate investigative reports for world events using open sources (e.g., social media). A particularly prominent example is Bellingcat. Bellingcat is known for its investigations on the illegal use of chemical weapons during the Syrian war, the Russian responsibility for downing flight MH17, the identification of the perpetrators in the attempted murder of Alexei Navalny, and war crimes in the Russo-Ukraine war. Crucial for this is social media in order to disseminate findings and crowdsource fact-checks. In this work, we characterize the social media activities at Bellingcat on Twitter. For this, we built a comprehensive dataset of all N =  24,682 tweets posted by Bellingcat on Twitter since its inception in July 2014. Our analysis is three-fold: (1) We analyze how Bellingcat uses Twitter to disseminate information and collect information from its follower base. Here, we find a steady increase in both posts and replies over time, particularly during the Russo-Ukrainian war, which is in line with the growing importance of Bellingcat for the traditional media ecosystem. (2) We identify characteristics of posts that are successful in eliciting user engagement. User engagement is particularly large for posts embedding additional media items and with a more negative sentiment. (3) We examine how the follower base has responded to the Russian invasion of Ukraine. Here, we find that the sentiment has become more polarized and negative. We attribute this to a ∼ 13-fold increase in bots interacting with the Bellingcat account. Overall, our findings provide recommendations for how open-source journalism such as Bellingcat can successfully operate on social media.

References

[1]
Kholoud Khalil Aldous, Jinsun An, and Bernard Jansen. 2019. Predicting audience engagement across social media platforms in the news domain. In Social Informatics. 173–187.
[2]
Kholoud Khalil Aldous, Jisun An, and Bernard J. Jansen. 2019. View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In ICWSM.
[3]
Stefan Baack. 2015. Datafication and empowerment: How the open data movement re-articulates notions of democracy, participation, and journalism. Big Data & Society 2, 2 (2015).
[4]
Christopher A. Bail, Brian Guay, Emily Maloney, Aidan Combs, D. Sunshine Hillygus, Friedolin Merhout, Deen Freelon, and Alexander Volfovsky. 2020. Assessing the Russian Internet Research Agency’s impact on the political attitudes and behaviors of American Twitter users in late 2017. PNAS 117, 1 (2020), 243–250.
[5]
Siva K. Balasubramanian, Mustafa Bilgic, Aron Culotta, Libby Hemphill, Anita Nikolich, and Matthew A. Shapiro. 2022. Leaders or followers? A temporal analysis of tweets from IRA trolls. In ICWSM.
[6]
Dominik Bär, N. Pröllochs, and Stefan Feuerriegel. 2023. Finding Qs: Profiling QAnon supporters on Parler. In ICWSM.
[7]
Dominik Bär, Nicolas Pröllochs, and Stefan Feuerriegel. 2023. New threats to society from free-speech social media platforms. arXiv 2302.01229(2023).
[8]
Bellingcat. 2022. Bellingcat. (2022). https://www.bellingcat.com/
[9]
Jonah Berger. 2011. Arousal increases social transmission of information. Psycological Science 22, 7 (2011), 891–893.
[10]
Jonah Berger and Katherine Milkman. 2012. What makes online content viral?Journal of Marketing Research. 49, 2 (2012), 192–205.
[11]
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. JMLR 3(2003), 993–1022.
[12]
Venkateswarlu Bonta, Nandhini Kumaresh, and N. Janardhan. 2019. A comprehensive study on lexicon based approaches for sentiment analysis. Asian Journal of Computer Science and Technology 8, S2(2019), 1–6.
[13]
Polaino Rafael Carrasco, Fernández Montse Mera, and Fernández Sonia Parratt. 2022. Journalists and engagement on Twitter and climate change: Tweet authors, formats, and content during COP25. Journalism Practice 16, 2-3 (2022), 486–501.
[14]
Patricia Cavazos-Rehg, M. J. Krauss, Shaina Sowles, Sarah Connolly, Carlos Rosas, Meghana Bharadwaj, and Laura Bierut. 2016. A content analysis of depression-related tweets. Computers in Human Behavior 54, C (2016), 351–357.
[15]
Justin Cheng, Michael Bernstein, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec. 2017. Anyone can become a troll: Causes of trolling behavior in online discussions. In CSCW.
[16]
Glenda Cooper and Bruce Mutsvairo. 2021. Citizen journalism : Is Bellingcat revolutionising conflict journalism?In Insights on Peace and Conflict Reporting. 106–120.
[17]
Aakash Desai, Jeremy Warner, Nicole Kuderer, Mike Thompson, Corrie Painter, Gary Lyman, and Gilberto Lopes. 2020. Crowdsourcing a crisis response for COVID-19 in oncology. Nature Cancer 1, 5 (2020), 473–476.
[18]
Sven Engesser and Edda Humprecht. 2015. Frequency or skillfulness. Journalism Studies 16, 4 (2015), 513–529.
[19]
Claudia Flores-Saviaga and Saiph Savage. 2021. Fighting disaster misinformation in Latin America: the #19S Mexican earthquake case study. Personal and Ubiquitous Computing 25, 2 (2021), 353–373.
[20]
Rupert Freeman, Sébastien Lahaie, and David Pennock. 2017. Crowdsourced outcome determination in prediction markets. In AAAI.
[21]
Huiji Gao, Geoffrey Barbier, and Rebecca Goolsby. 2011. Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intelligent Systems 26, 3 (2011), 10–14.
[22]
Dominique Geissler, Dominik Bär, Nicolas Pröllochs, and Stefan Feuerriegel. 2022. Russian propaganda on social media during the 2022 invasion of Ukraine. arXiv 2211.04154(2022).
[23]
Carlos Gonzales. 2022. Creating Impact: A year on stop child abuse: Trace an object. Bellingcat (2022). https://www.bellingcat.com/news/uk-and-europe/2020/04/22/creating-impact-a-year-on-stop-child-abuse-trace-an-object/
[24]
Hans W. A. Hanley, Deepak Kumar, and Zakir Durumeric. 2022. Happenstance: Utilizing semantic search to track Russian state media narratives about the Russo-Ukrainian war on Reddit. arXiv 2205.14484(2022).
[25]
Lars Kai Hansen, Adam Arvidsson, Finn Aarup Nielsen, Elanor Colleoni, and Michael Etter. 2011. Good friends, bad news - Affect and virality in Twitter. In Future Information Technology. 34–43.
[26]
Jakob Hauter. 2022. Forensic conflict studies: Making sense of war in the social media age. Media, War & Conflict(2022).
[27]
Eliot Higgins. 2021. We are Bellingcat: An intelligence agency for the people. Bloomsbury Publishing, London.
[28]
Howe Jeff. 2008. Crowdsourcing: How the power of the crowd is driving the future of business. Currency, Sydney.
[29]
Muhammad Fahad Humayun and Patrick Ferrucci. 2022. Understanding social media in journalism practice: A typology. Digital Journalism (2022).
[30]
Amelia Hunt and Doug Specht. 2019. Crowdsourced mapping in crisis zones: collaboration, organisation and impact. Journal of International Humanitarian Action 4 (2019), 1.
[31]
C. J. Hutto and Eric Gilbert. 2014. Vader: A parsimonious rule-based model for Sentiment Analysis of social media text. In ICWSM.
[32]
Yi-Ling Hwong, Carol Oliver, Martin van Kranendonk, Claude Sammut, and Yanir Seroussi. 2017. What makes you tick? The psychology of social media engagement in space science communication. Computers in Human Behavior 68 (2017), 480–492.
[33]
Yuliya Ilyuk. 2019. Journalistic investigations in the digital age of post-truth politics: The analysis of Bellingcat’s research approaches used for the (re)construction of the MH17 case. Perekrestki1(2019), 56–78.
[34]
Johannes Jakubik, Michael Vössing, Dominik Bär, Nicolas Pröllochs, and Stefan Feuerriegel. 2023. Online emotions during the storming of the US capitol: Evidence from the social media network Parler. In ICWSM.
[35]
Maximilian Jenders, Gjergji Kasneci, and Felix Naumann. 2013. Analyzing and predicting viral tweets. In WWW.
[36]
Srijan Kumar, William L. Hamilton, Jure Leskovec, and Dan Jurafsky. 2018. Community interaction and conflict on the web. In WWW.
[37]
J. Richard Landis and Gary G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics 33, 1 (1977), 159–174.
[38]
Seth C. Lewis and Nikki Usher. 2013. Open source and journalism: Toward new frameworks for imagining news innovation. Media, Culture & Society 35, 5 (2013), 602–619.
[39]
Yiyi Li and Ying Xie. 2020. Is a picture worth a thousand words? An empirical study of image Content and social media engagement. Journal of Marketing Research 57, 1 (2020), 1–19.
[40]
Chun-Ta Lu, Sihong Xie, Xiangnan Kong, and Philip S. Yu. 2014. Inferring the impacts of social media on crowdfunding. In WSDM.
[41]
Abdurahman Maarouf, Nicolas Pröllochs, and Stefan Feuerriegel. 2022. The virality of hate speech on social media. arXiv 2210.13770(2022).
[42]
Patrick Meier. 2012. Crisis mapping in action: How open source software and global volunteer networks are changing the world, one map at a time. Journal of Map & Geography Libraries 8, 2 (2012), 89–100.
[43]
Saif M. Mohammad. 2021. Sentiment analysis: Automatically detecting valence, emotions, and other affectual states from text. In Emotion Measurement (Second Edition). 323–379.
[44]
Femke Mulder, Julie Ferguson, Peter Groenewegen, Kees Boersma, and Jeroen Wolbers. 2016. Questioning big data: Crowdsourcing crisis data towards an inclusive humanitarian response. Big Data & Society 3, 2 (2016), 1–13.
[45]
Nina C. Müller and Jenny Wiik. 2021. From gatekeeper to gate-opener: Open-source spaces in investigative journalism. Journalism Practice (2021).
[46]
Robert Munro. 2013. Crowdsourcing and the crisis-affected community. Information Retrieval 16, 2 (2013), 210–266.
[47]
Bruce Mutsvairo and Susana Salgado. 2022. Is citizen journalism dead? An examination of recent developments in the field. Journalism 23, 2 (2022), 354–371.
[48]
Nasir Naveed, Thomas Gottron, Jérôme Kunegis, and Arifah Che Alhadi. 2011. Bad news travel fast: A content-based analysis of interestingness on Twitter. In WebSci.
[49]
Katherine Ognyanova, David Lazer, Ronald E. Robertson, and Christo Wilson. 2020. Misinformation in action: Fake news exposure is linked to lower trust in media, higher trust in government when your side is in power. Harvard Kennedy School Misinformation Review 1, 4 (2020).
[50]
Claudia Orellana-Rodriguez, Derek Greene, and M. T. Keane. 2016. Spreading the news: How can journalists gain more engagement for their Tweets?WebSci (2016).
[51]
Scott Pelley. 2022. Bellingcat: The online investigators tracking alleged Russian war crimes in Ukraine - 60 Minutes. CBS News (2022). https://www.cbsnews.com/news/bellingcat-russia-putin-ukraine-60-minutes-2022-08-21/
[52]
James W. Pennebaker, Ryan L. Boyd, Kayla Jordan, and Kate Blackburn. 2015. The development and psychometric properties of LIWC2015. In Texas ScholarWorks.
[53]
Nicolas Pröllochs, Dominik Bär, and Stefan Feuerriegel. 2021. Emotions explain differences in the diffusion of true vs. false social media rumors. Scientific Reports 11, 1 (2021).
[54]
Nicolas Pröllochs, Dominik Bär, and Stefan Feuerriegel. 2021. Emotions in online rumor diffusion. EPJ Data Science 10, 51 (2021).
[55]
Nicolas Pröllochs and Stefan Feuerriegel. 2022. Mechanisms of true and false rumor sharing in social media: Wisdom-of-crowds or herd behavior?arXiv 2207.03020(2022).
[56]
Sandra Ristovska. 2022. Open-source investigation as a genre of conflict reporting. Journalism 23, 3 (2022), 632–648.
[57]
Caitlin M. Rivers and Bryan L. Lewis. 2014. Ethical research standards in a world of big data. F1000Research 3, 38 (2014).
[58]
Claire Robertson, Nicolas Pröllochs, Kaoru Schwarzenegger, Phillip Parnamets, Jay J. van Bavel, and Stefan Feuerriegel. 2022. Negativity drives online news consumption. Nature Human Behaviour(2022).
[59]
Anja Rudat and Jürgen Buder. 2015. Making retweeting social: The influence of content and context information on sharing news in Twitter. Computers in Human Behavior 46 (2015), 75–84.
[60]
Frank Michael Russell. 2019. Twitter and news gatekeeping. Digital Journalism 7, 1 (2019), 80–99.
[61]
Gery W. Ryan and H. Russell Bernard. 2003. Techniques to identify themes. Field Methods 15, 1 (2003), 85–109.
[62]
Mohsen Sayyadiharikandeh, Onur Varol, Kai-Cheng Yang, Alessandro Flammini, and Filippo Menczer. 2020. Detection of novel social bots by ensembles of specialized classifiers. In CIKM.
[63]
Gerald Schimak, Denis Havlik, and Jasmin Pielorz. 2015. Crowdsourcing in crisis and disaster management: Challenges and considerations. In Environmental Software Systems. Infrastructures, Services and Applications. 56–70.
[64]
Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Kai-Cheng Yang, Alessandro Flammini, and Filippo Menczer. 2018. The Spread of Low-Credibility Content by Social Bots. Nature Communications 9(2018), 4787.
[65]
Yotam Shmargad, Kevin Coe, Kate Kenski, and Stephen A. Rains. 2022. Social norms and the dynamics of online incivility. Social Science Computer Review 40, 3 (2022), 717–735.
[66]
Matt Sienkiewicz. 2014. Start making sense: a three-tier approach to citizen journalism. Media, Culture & Society 36, 5 (2014), 691–701.
[67]
Peter Warren Singer and Emerson T. Brooking. 2018. LikeWar: The weaponization of social media. Eamon Dolan Books, Boston.
[68]
N. V. Smirnov. 1939. Estimate of deviation between empirical distribution funcitons in two independent samples. Bulletin Moscow University 2, 2 (1939), 3–16.
[69]
Stuart Soroka, Patrick Fournier, and Lilach Nir. 2019. Cross-national evidence of a negativity bias in psychophysiological reactions to news. PNAS 116, 38 (2019), 18888–18892.
[70]
Stefan Stieglitz and Linh Dang-Xuan. 2013. Emotions and information diffusion in social media: Sentiment of microblogs and sharing behavior. Journal of Management Information Systems 29, 4 (2013), 217–248.
[71]
Bongwon Suh, Lichan Hong, Peter Pirolli, and Ed H. Chi. 2010. Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. In SocialCom.
[72]
Malte Toetzke, Nicolas Banholzer, and Stefan Feuerriegel. 2022. Monitoring global development aid with machine learning. Nature Sustainability 5, 6 (2022), 533–541.
[73]
Mason Walker and Katerina Eva Matsa. 2021. News consumption across social media in 2021. Pew Research (2021). https://www.pewresearch.org/journalism/2021/09/20/news-consumption-across-social-media-in-2021/
[74]
Stefan Wojcik, Solomon Messing, Aaron W. Smith, Lee Rainie, and Paul Hitlin. 2018. Bots in the Twittersphere. Pew Research (2018). https://www.pewresearch.org/internet/2018/04/09/bots-in-the-twittersphere/

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  • (2023)Assessing the Vulnerability of Military Personnel Through Open Source Intelligence: A Case Study of Lithuanian Armed ForcesHCI International 2023 – Late Breaking Papers10.1007/978-3-031-48057-7_27(435-444)Online publication date: 23-Jul-2023

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cover image ACM Conferences
WebSci '23: Proceedings of the 15th ACM Web Science Conference 2023
April 2023
373 pages
ISBN:9798400700897
DOI:10.1145/3578503
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 30 April 2023

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Author Tags

  1. Bellingcat
  2. Open Source Journalism
  3. Russian Invasion
  4. Social Media
  5. Twitter

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WebSci '23: 15th ACM Web Science Conference 2023
April 30 - May 1, 2023
TX, Austin, USA

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  • (2023)Assessing the Vulnerability of Military Personnel Through Open Source Intelligence: A Case Study of Lithuanian Armed ForcesHCI International 2023 – Late Breaking Papers10.1007/978-3-031-48057-7_27(435-444)Online publication date: 23-Jul-2023

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