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

  • Zhiwei JinEmail author
  • Juan Cao
  • Han Guo
  • Yongdong Zhang
  • Yu Wang
  • Jiebo Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)

Abstract

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.

Keywords

Presidential Election Presidential Candidate Label Training Data Election Period Favored Candidate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0800403 and the National Nature Science Foundation of China (61571424, 61525206). Jiebo Luo and Yu Wang would like to thank the support from the New York State through the Goergen Institute for Data Science. Zhiwei Jin gratefully thanks the sponsorship from the China Scholarship Council.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zhiwei Jin
    • 1
    • 2
    Email author
  • Juan Cao
    • 1
    • 2
  • Han Guo
    • 1
    • 2
  • Yongdong Zhang
    • 1
    • 2
  • Yu Wang
    • 3
  • Jiebo Luo
    • 3
  1. 1.Key Laboratory of Intelligent Information ProcessingInstitute of Computing Technology, CASBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.University of RochesterRochesterUSA

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