Hong Zhang
Haewoon Kwak
ABSTRACT
People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well. The existence of such connections, which we call connected behavior, gives researchers a unique opportunity to predict how one would behave for a future event given their past behaviors. In this work, we propose a framework to conduct connected behavior analysis. Neural stance detection models are trained on Twitter data collected on three seemingly independent topics, i.e., wearing a mask, racial equality, and Trump, to detect people’s stance, which we consider as their online behavior in each topic-related event. Our results reveal a strong connection between the stances toward the three topical events and demonstrate the power of past behaviors in predicting one’s future behavior.
- 2020. US election: Trump says Biden won but again refuses to concede. BBC (Nov 2020). https://www.bbc.com/news/election-us-2020-54952098Google Scholar
- 2022. Twitter API for academic research | products | twitter developer platform. https://developer.twitter.com/en/products/twitter-api/academic-researchGoogle Scholar
- Christopher Adolph, Kenya Amano, Bree Bang-Jensen, Nancy Fullman, Beatrice Magistro, Grace Reinke, and John Wilkerson. 2022. Governor partisanship explains the adoption of statewide mask mandates in response to COVID-19. State Politics & Policy Quarterly 22, 1 (2022), 24–49.Google ScholarCross Ref
- Jisun An, Haewoon Kwak, Claire Seungeun Lee, Bogang Jun, and Yong-Yeol Ahn. 2021. Predicting Anti-Asian Hateful Users on Twitter during COVID-19. In Findings of the Association for Computational Linguistics: EMNLP 2021.Google ScholarCross Ref
- Jisun An, Daniele Quercia, and Jon Crowcroft. 2014. Partisan sharing: Facebook evidence and societal consequences. In Proceedings of the second ACM conference on Online social networks. 13–24.Google ScholarDigital Library
- Jisun An and Ingmar Weber. 2016. # greysanatomy vs.# yankees: Demographics and Hashtag Use on Twitter. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 10. 523–526.Google Scholar
- Pablo Barberá, John T Jost, Jonathan Nagler, Joshua A Tucker, and Richard Bonneau. 2015. Tweeting from left to right: Is online political communication more than an echo chamber?Psychological science 26, 10 (2015), 1531–1542.Google ScholarCross Ref
- Ross Barkan. 2020. High-profile Killings of Unarmed Black People Spark Calls for Reform. ABAJ 106 (2020), 14–16.Google Scholar
- Hila Becker, Mor Naaman, and Luis Gravano. 2011. Beyond trending topics: Real-world event identification on twitter. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 5. 438–441.Google Scholar
- Li Bing, Keith CC Chan, and Carol Ou. 2014. Public sentiment analysis in Twitter data for prediction of a company’s stock price movements. In 2014 IEEE 11th International Conference on e-Business Engineering. IEEE, 232–239.Google ScholarDigital Library
- Chao Chen, Yu Wang, Jun Zhang, Yang Xiang, Wanlei Zhou, and Geyong Min. 2016. Statistical features-based real-time detection of drifted Twitter spam. IEEE Transactions on Information Forensics and Security 12, 4 (2016), 914–925.Google ScholarCross Ref
- Hsinchun Chen and David Zimbra. 2010. AI and opinion mining. IEEE Intelligent Systems 25, 3 (2010), 74–80.Google ScholarDigital Library
- Matteo Cinelli, Gianmarco De Francisci Morales, Alessandro Galeazzi, Walter Quattrociocchi, and Michele Starnini. 2021. The echo chamber effect on social media. Proceedings of the National Academy of Sciences 118, 9 (2021).Google Scholar
- Elanor Colleoni, Alessandro Rozza, and Adam Arvidsson. 2014. Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of communication 64, 2 (2014), 317–332.Google ScholarCross Ref
- Michael D Conover, Bruno Gonçalves, Jacob Ratkiewicz, Alessandro Flammini, and Filippo Menczer. 2011. Predicting the political alignment of twitter users. In 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing. IEEE, 192–199.Google Scholar
- Kareem Darwish, Peter Stefanov, Michaël Aupetit, and Preslav Nakov. 2020. Unsupervised user stance detection on twitter. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 14. 141–152.Google ScholarCross Ref
- Clayton Allen Davis, Onur Varol, Emilio Ferrara, Alessandro Flammini, and Filippo Menczer. 2016. Botornot: A system to evaluate social bots. In Proceedings of the 25th international conference companion on world wide web. 273–274. https://doi.org/10.1145/2872518.2889302Google ScholarDigital Library
- Michela Del Vicario, Alessandro Bessi, Fabiana Zollo, Fabio Petroni, Antonio Scala, Guido Caldarelli, H Eugene Stanley, and Walter Quattrociocchi. 2016. The spreading of misinformation online. Proceedings of the National Academy of Sciences 113, 3 (2016), 554–559.Google ScholarCross Ref
- Drew Desilver. 2021. Turnout soared in 2020 as nearly two-thirds of eligible U.S. voters cast ballots for president. Pew Research Center (Jan 2021). https://www.pewresearch.org/fact-tank/2021/01/28/turnout-soared-in-2020-as-nearly-two-thirds-of-eligible-u-s-voters-cast-ballots-for-president/Google Scholar
- Marco Di Giovanni and Marco Brambilla. 2021. Content-based Stance Classification of Tweets about the 2020 Italian Constitutional Referendum. In Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media. 14–23.Google ScholarCross Ref
- Xing Fang and Justin Zhan. 2015. Sentiment analysis using product review data. Journal of Big Data 2, 1 (2015), 1–14.Google ScholarCross Ref
- Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. 2016. The rise of social bots. Commun. ACM 59, 7 (2016), 96–104.Google ScholarDigital Library
- Shalmoli Ghosh, Prajwal Singhania, Siddharth Singh, Koustav Rudra, and Saptarshi Ghosh. 2019. Stance detection in web and social media: a comparative study. In International Conference of the Cross-Language Evaluation Forum for European Languages. Springer, 75–87.Google ScholarDigital Library
- Bo Han and Timothy Baldwin. 2011. Lexical normalisation of short text messages: Makn sens a# twitter. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies. 368–378.Google Scholar
- Maryam Hasan, Emmanuel Agu, and Elke Rundensteiner. 2014. Using hashtags as labels for supervised learning of emotions in twitter messages. In Acm sigkdd workshop on health informatics, new york, usa.Google Scholar
- Sounman Hong. 2013. Who benefits from Twitter? Social media and political competition in the US House of Representatives. Government Information Quarterly 30, 4 (2013), 464–472.Google ScholarCross Ref
- Jake Horton, Sarah Glatte, and Soraya Auer. 2020. US election 2020: How much did it cost and who paid for it?BBC (Nov 2020). https://www.bbc.com/news/av/election-us-2020-54696386Google Scholar
- Yuki Igarashi, Hiroya Komatsu, Sosuke Kobayashi, Naoaki Okazaki, and Kentaro Inui. 2016. Tohoku at SemEval-2016 task 6: Feature-based model versus convolutional neural network for stance detection. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). 401–407.Google ScholarCross Ref
- Leo H Kahane. 2021. Politicizing the mask: Political, economic and demographic factors affecting mask wearing behavior in the USA. Eastern economic journal 47, 2 (2021), 163–183.Google Scholar
- Nathan Kallus. 2014. Predicting crowd behavior with big public data. In Proceedings of the 23rd International Conference on World Wide Web. 625–630.Google ScholarDigital Library
- Thomas Kaplan and D. Shear Michael. 2020. Biden and Trump will debate six main issues. here’s where they stand.https://www.nytimes.com/2020/09/29/us/politics/biden-on-the-issues-trump-on-the-issues-debate.htmlGoogle Scholar
- Vishal Kharde, Prof Sonawane, 2016. Sentiment analysis of twitter data: a survey of techniques. arXiv preprint arXiv:1601.06971 (2016).Google Scholar
- Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is Twitter, a social network or a news media?. In Proceedings of the 19th international conference on World wide web. 591–600.Google ScholarDigital Library
- Mirko Lai, Alessandra Teresa Cignarella, Delia Irazú Hernández Farías, Cristina Bosco, Viviana Patti, and Paolo Rosso. 2020. Multilingual stance detection in social media political debates. Computer Speech & Language 63 (2020), 101075.Google ScholarCross Ref
- Claire Seungeun Lee, Juan Merizalde, John D Colautti, Jisun An, and Haewoon Kwak. 2022. Storm the Capitol: Linking Offline Political Speech and Online Twitter Extra-Representational Participation on QAnon and the January 6 Insurrection. Frontiers in Sociology 7 (2022).Google Scholar
- Claire Seungeun Lee, Juan Merizalde, John D. Colautti, Jisun An, and Haewoon Kwak. 2022. Storm the Capitol: Linking Offline Political Speech and Online Twitter Extra-Representational Participation on QAnon and the January 6 Insurrection. Frontiers in Sociology 7 (2022). https://doi.org/10.3389/fsoc.2022.876070Google ScholarCross Ref
- Jing Ma, Wei Gao, and Kam-Fai Wong. 2018. Detect rumor and stance jointly by neural multi-task learning. In Companion proceedings of the the web conference 2018. 585–593.Google ScholarDigital Library
- Walid Magdy, Hassan Sajjad, Tarek El-Ganainy, and Fabrizio Sebastiani. 2015. Distant supervision for tweet classification using youtube labels. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 9. 638–641.Google Scholar
- Jalal Mahmud, Geli Fei, Anbang Xu, Aditya Pal, and Michelle Zhou. 2016. Predicting attitude and actions of twitter users. In Proceedings of the 21st International Conference on Intelligent User Interfaces. 2–6.Google ScholarDigital Library
- Ardian Maulana and Hokky Situngkir. 2020. Measuring Media Partisanship during Election: The Case of 2019 Indonesia Election. (2020).Google Scholar
- Paula D. McClain. 2021. Trump and racial equality in America? No pretense at all!Policy Studies 42 (2021), 491–508.Google Scholar
- Yelena Mejova, Jisun An, Gianmarco De Francisci Morales, and Haewoon Kwak. 2022. Modeling political activism around gun debate via social media. Transactions on Social Computing 5, 1-4 (2022), 1–28.Google ScholarDigital Library
- Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016. Semeval-2016 task 6: Detecting stance in tweets. In Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016). 31–41.Google ScholarCross Ref
- DOMENICO MONTANARO. 2020. Majority Of Americans Say Trump Increased Racial Tensions After George Floyd’s Death, Poll Finds. The New York Times Company (Jun 2020). https://www.npr.org/2020/06/06/871404645/majority-of-americans-say-trump-increased-racial-tensions-after-george-floyds-deGoogle Scholar
- Brendan O’Connor, Ramnath Balasubramanyan, Bryan R Routledge, and Noah A Smith. 2010. From tweets to polls: Linking text sentiment to public opinion time series. In Fourth international AAAI conference on weblogs and social media.Google ScholarCross Ref
- Michaël Opgenhaffen and Harald Scheerlinck. 2014. Social media guidelines for journalists: An investigation into the sense and nonsense among Flemish journalists. Journalism Practice 8, 6 (2014), 726–741.Google ScholarCross Ref
- Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12 (2011), 2825–2830.Google Scholar
- Sarvesh Ranade, Rajeev Sangal, and Radhika Mamidi. 2013. Stance classification in online debates by recognizing users’ intentions. In Proceedings of the SIGDIAL 2013 Conference. 61–69.Google Scholar
- Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019).Google Scholar
- Younes Samih and Kareem Darwish. 2020. A few topical tweets are enough for effective user-level stance detection. arXiv preprint arXiv:2004.03485 (2020).Google Scholar
- Jieying She and Lei Chen. 2014. Tomoha: Topic model-based hashtag recommendation on twitter. In Proceedings of the 23rd international conference on World Wide Web. 371–372.Google ScholarDigital Library
- Jieun Shin, Aimei Yang, Wenlin Liu, Hye Min Kim, Alvin Zhou, and Jingyi Sun. 2022. Mask-wearing as a partisan issue: Social identity and communication of party norms on social media among political elites. Social Media+ Society 8, 1 (2022), 20563051221086233.Google Scholar
- Tamara A Small. 2011. What the hashtag? A content analysis of Canadian politics on Twitter. Information, communication & society 14, 6 (2011), 872–895.Google Scholar
- Parinaz Sobhani, Diana Inkpen, and Xiaodan Zhu. 2017. A dataset for multi-target stance detection. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. 551–557.Google ScholarCross Ref
- Derrick Bryson Taylor. 2021. George Floyd Protests: A Timeline. The New York Times Company (Nov 2021). https://www.nytimes.com/article/george-floyd-protests-timeline.htmlGoogle Scholar
- Ted Van Green and Alec Tyson. 2020. facts about partisan reactions to COVID-19 in the US. Pew Research Center 2 (2020).Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).Google Scholar
- Marilyn Walker, Pranav Anand, Rob Abbott, and Ricky Grant. 2012. Stance classification using dialogic properties of persuasion. In Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: Human language technologies. 592–596.Google ScholarDigital Library
- Hao Wang, Doğan Can, Abe Kazemzadeh, François Bar, and Shrikanth Narayanan. 2012. A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In Proceedings of the ACL 2012 system demonstrations. 115–120.Google ScholarDigital Library
- Zhiguo Wang, Patrick Ng, Xiaofei Ma, Ramesh Nallapati, and Bing Xiang. 2019. Multi-passage bert: A globally normalized bert model for open-domain question answering. arXiv preprint arXiv:1908.08167 (2019).Google Scholar
- Stevan Weine, Brandon A Kohrt, Pamela Y Collins, Janice Cooper, Roberto Lewis-Fernandez, Samuel Okpaku, and Milton L Wainberg. 2020. Justice for George Floyd and a reckoning for global mental health. Global Mental Health 7 (2020).Google Scholar
- Guobin Yang. 2016. Narrative agency in hashtag activism: The case of# BlackLivesMatter. Media and communication 4, 4 (2016), 13.Google Scholar
- Dannagal G Young, Huma Rasheed, Amy Bleakley, and Jessica B Langbaum. 2022. The politics of mask-wearing: Political preferences, reactance, and conflict aversion during COVID. Social Science & Medicine (2022), 114836.Google Scholar
- Zhuosheng Zhang, Yuwei Wu, Hai Zhao, Zuchao Li, Shuailiang Zhang, Xi Zhou, and Xiang Zhou. 2020. Semantics-aware BERT for language understanding. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 9628–9635.Google ScholarCross Ref
Index Terms
- Wearing Masks Implies Refuting Trump?: Towards Target-specific User Stance Prediction across Events in COVID-19 and US Election 2020
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