Detection and Analysis of 2016 US Presidential Election Related Rumors on Twitter

The 2016 U.S. presidential election has witnessed the major role of Twitter in the year’s most important political event. Candidates used this social media platform extensively for online campaigns. Meanwhile, social media has been filled with rumors, which might have had huge impacts on voters’ decisions. In this paper, we present a thorough analysis of rumor tweets from the followers of two presidential candidates: Hillary Clinton and Donald Trump. To overcome the difficulty of labeling a large amount of tweets as training data, we detect rumor tweets by matching them with verified rumor articles. We analyze over 8 million tweets collected from the followers of the two candidates. Our results provide answers to several primary concerns about rumors in this election, including: which side of the followers posted the most rumors, who posted these rumors, what rumors they posted, and when they posted these rumors. The insights of this paper can help us understand the online rumor behaviors in American politics.

[1]  Qiaozhu Mei,et al.  Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts , 2015, WWW.

[2]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[3]  Yongdong Zhang,et al.  News Credibility Evaluation on Microblog with a Hierarchical Propagation Model , 2014, 2014 IEEE International Conference on Data Mining.

[4]  Scott Counts,et al.  Tweeting is believing?: understanding microblog credibility perceptions , 2012, CSCW.

[5]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[6]  Karen Spärck Jones.  A statistical interpretation of term specificity and its application in retrieval , 2021, J. Documentation.

[7]  Hugo Zaragoza,et al.  The Probabilistic Relevance Framework: BM25 and Beyond , 2009, Found. Trends Inf. Retr..

[8]  P. Bordia,et al.  Rumor Psychology: Social and Organizational Approaches , 2006 .

[9]  Kenny Q. Zhu,et al.  False rumors detection on Sina Weibo by propagation structures , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[10]  Edward A. Fox,et al.  Research Contributions , 2014 .

[11]  Jiawei Han,et al.  Evaluating Event Credibility on Twitter , 2012, SDM.

[12]  Jiebo Luo,et al.  Catching Fire via "Likes": Inferring Topic Preferences of Trump Followers on Twitter , 2016, ICWSM.

[13]  Yongdong Zhang,et al.  Novel Visual and Statistical Image Features for Microblogs News Verification , 2017, IEEE Transactions on Multimedia.

[14]  Shrikanth S. Narayanan,et al.  A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle , 2012, ACL.

[15]  Yongdong Zhang,et al.  News Verification by Exploiting Conflicting Social Viewpoints in Microblogs , 2016, AAAI.

[16]  Yongdong Zhang,et al.  MCG-ICT at MediaEval 2015: Verifying Multimedia Use with a Two-Level Classification Model , 2015, MediaEval.

[17]  Justin Cheng,et al.  Rumor Cascades , 2014, ICWSM.

[18]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.

[19]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[20]  Kyomin Jung,et al.  Prominent Features of Rumor Propagation in Online Social Media , 2013, 2013 IEEE 13th International Conference on Data Mining.