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Why watching movie tweets won't tell the whole story?

Authors:

Felix Ming Fai Wong,

Soumya Sen,

Mung ChiangAuthors Info & Claims

WOSN '12: Proceedings of the 2012 ACM workshop on Workshop on online social networks

Pages 61 - 66

https://doi.org/10.1145/2342549.2342564

Published: 17 August 2012 Publication History

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Abstract

Data from Online Social Networks (OSNs) are providing analysts with an unprecedented access to public opinion on elections, news, movies, etc. However, caution must be taken to determine whether and how much of the opinion extracted from OSN user data is indeed reflective of the opinion of the larger online population. In this work we study this issue in the context of movie reviews on Twitter and compare the opinion of Twitter users with that of IMDb and Rotten Tomatoes. We introduce metrics to quantify how Twitter users can be characteristically different from general users, both in their rating and their relative preference for Oscar-nominated and non-nominated movies. We also investigate whether such data can truly predict a movie's box-office success.

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Cited By

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Simon FSchroeder R(2019)Big Data Goes to Hollywood: The Emergence of Big Data as a Tool in the American Film IndustrySecond International Handbook of Internet Research10.1007/978-94-024-1555-1_63(549-567)Online publication date: 10-Oct-2019
https://doi.org/10.1007/978-94-024-1555-1_63
Simon FSchroeder R(2019)Big Data Goes to Hollywood: The Emergence of Big Data as a Tool in the American Film IndustrySecond International Handbook of Internet Research10.1007/978-94-024-1202-4_63-1(1-20)Online publication date: 27-May-2019
https://doi.org/10.1007/978-94-024-1202-4_63-1
Ruus RSharma R(2019)Predicting Movies’ Box Office Result - A Large Scale Study Across Hollywood and BollywoodComplex Networks and Their Applications VIII10.1007/978-3-030-36683-4_78(982-994)Online publication date: 25-Nov-2019
https://doi.org/10.1007/978-3-030-36683-4_78
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Why watching movie tweets won't tell the whole story?

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Information & Contributors

Information

Published In

WOSN '12: Proceedings of the 2012 ACM workshop on Workshop on online social networks

August 2012

80 pages

ISBN:9781450314800

DOI:10.1145/2342549

Conference Chair:
Balachander Krishnamurthy
AT&T Labs-Research
,
Program Chairs:
Stefan Saroiu
Microsoft Research Redmond, USA
,
Ben Zhao
UC Santa Barbara, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2012

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SIGCOMM '12

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SIGCOMM

SIGCOMM '12: ACM SIGCOMM 2012 Conference

August 17, 2012

Helsinki, Finland

Acceptance Rates

WOSN '12 Paper Acceptance Rate 12 of 36 submissions, 33%;

Overall Acceptance Rate 12 of 36 submissions, 33%

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Cited By

View all

Simon FSchroeder R(2019)Big Data Goes to Hollywood: The Emergence of Big Data as a Tool in the American Film IndustrySecond International Handbook of Internet Research10.1007/978-94-024-1555-1_63(549-567)Online publication date: 10-Oct-2019
https://doi.org/10.1007/978-94-024-1555-1_63
Simon FSchroeder R(2019)Big Data Goes to Hollywood: The Emergence of Big Data as a Tool in the American Film IndustrySecond International Handbook of Internet Research10.1007/978-94-024-1202-4_63-1(1-20)Online publication date: 27-May-2019
https://doi.org/10.1007/978-94-024-1202-4_63-1
Ruus RSharma R(2019)Predicting Movies’ Box Office Result - A Large Scale Study Across Hollywood and BollywoodComplex Networks and Their Applications VIII10.1007/978-3-030-36683-4_78(982-994)Online publication date: 25-Nov-2019
https://doi.org/10.1007/978-3-030-36683-4_78
Ramachandran AWang LChaintreau ALee DSastry NWeber I(2018)Dynamics and Prediction of Clicks on News from TwitterProceedings of the 29th on Hypertext and Social Media10.1145/3209542.3209568(210-214)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209542.3209568
Nanda MPattnaik CLu Q(2018)Innovation in social media strategy for movie successManagement Decision10.1108/MD-04-2017-042956:1(233-251)Online publication date: 8-Jan-2018
https://doi.org/10.1108/MD-04-2017-0429
Bozanta AKutlu B(2018)Do Twitter phenomena check-in popular venues on Foursquare too?Information Discovery and Delivery10.1108/IDD-04-2018-001246:3(137-146)Online publication date: 20-Aug-2018
https://doi.org/10.1108/IDD-04-2018-0012
Vujić SZhang X(2018)Does Twitter chatter matter? Online reviews and box office revenuesApplied Economics10.1080/00036846.2018.143614850:34-35(3702-3717)Online publication date: 11-Feb-2018
https://doi.org/10.1080/00036846.2018.1436148
Oh CRoumani YNwankpa JHu H(2017)Beyond likes and tweetsInformation and Management10.1016/j.im.2016.03.00454:1(25-37)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.im.2016.03.004
Zimbra DSarangee KJindal R(2017)Movie aspects, tweet metrics, and movie revenuesDecision Support Systems10.1016/j.dss.2017.08.002102:C(98-109)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1016/j.dss.2017.08.002
Ding CCheng HDuan YJin Y(2017)The power of the like buttonDecision Support Systems10.1016/j.dss.2016.11.00294:C(77-84)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1016/j.dss.2016.11.002
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Objective Evaluation or Subjective Evaluation in Digital Social Media: A further understanding of online user's rating of movies

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