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Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics.

, , , and . NLP+CSS@ACL, page 32-41. Association for Computational Linguistics, (2017)

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Categorization and Comparison of Influential Twitter Users and Sources Referenced in Tweets for Two Health-Related Topics., , and . iConference, volume 11420 of Lecture Notes in Computer Science, page 639-646. Springer, (2019)Users' Attitudes Towards the "Going Dark" Debate., , and . AHFE (9), volume 593 of Advances in Intelligent Systems and Computing, page 296-307. Springer, (2017)Characterizing the 2016 Russian IRA Influence Campaign., , , and . CoRR, (2018)"What Is Your Evidence?" A Study of Controversial Topics on Social Media., and . ArgMining@ACL, The Association for Computer Linguistics, (2016)Understanding Ordinary and Disruptive Events Discussion in Twitter: Barbados Environmental Health Hazard as a Use Case., and . IHSI, volume 903 of Advances in Intelligent Systems and Computing, page 319-325. Springer, (2019)Linguistic Cues to Deception: Identifying Political Trolls on Social Media., , , and . ICWSM, page 15-25. AAAI Press, (2019)Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics., , , and . NLP+CSS@ACL, page 32-41. Association for Computational Linguistics, (2017)Characterizing the 2016 Russian IRA influence campaign., , , and . Soc. Netw. Anal. Min., 9 (1): 31:1-31:11 (2019)Understanding misinformation on Twitter in the context of controversial issues. University of Illinois Urbana-Champaign, USA, (2019)Tracking And Understanding Public Reaction During COVID-19: Saudi Arabia As A Use Case., , , , , , and . NLP4COVID@EMNLP, Association for Computational Linguistics, (2020)