Abstract
Which topics spark the most heated debates on social media? Identifying those topics is not only interesting from a societal point of view but also allows the filtering and aggregation of social media content for disseminating news stories. In this article, we perform a systematic methodological study of controversy detection by using the content and the network structure of social media.
Unlike previous work, rather than studying controversy in a single hand-picked topic and using domain-specific knowledge, we take a general approach to study topics in any domain. Our approach to quantifying controversy is based on a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic, (ii) partitioning the conversation graph to identify potential sides of the controversy, and (iii) measuring the amount of controversy from characteristics of the graph.
We perform an extensive comparison of controversy measures, different graph-building approaches, and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy and show that content features are vastly less helpful in this task.
- Lada A. Adamic and Natalie Glance. 2005. The political blogosphere and the 2004 US election: Divided they blog. In Proceedings of the 3rd International Workshop on Link Discovery. ACM, 36--43. Google ScholarDigital Library
- Leman Akoglu. 2014. Quantifying political polarity based on bipartite opinion networks. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’14).Google Scholar
- M. D. Tanvir Al Amin, Charu Aggarwal, Shuochao Yao, Tarek Abdelzaher, and Lance Kaplan. 2017. Unveiling Polarization in Social Networks: A Matrix Factorization Approach. Technical Report. IEEE.Google Scholar
- Jisun An, Daniele Quercia, and Jon Crowcroft. 2014. Partisan sharing: Facebook evidence and societal consequences. In Proceedings of the Consortium for School Networking (COSN’14). 13--24. Google ScholarDigital Library
- Alessandro Bessi, Guido Caldarelli, Michela Del Vicario, Antonio Scala, and Walter Quattrociocchi. 2014. Social determinants of content selection in the age of (mis)information. In Social Informatics. 259--268.Google Scholar
- Aaron Bramson, Patrick Grim, Daniel J. Singer, Steven Fisher, William Berger, Graham Sack, and Carissa Flocken. 2016. Disambiguation of social polarization concepts and measures. J. Math. Sociol. 40, 2 (2016), 80--111.Google ScholarCross Ref
- Ronald S. Burt. 2009. Structural Holes: The Social Structure of Competition. Harvard University Press.Google Scholar
- Yoonjung Choi, Yuchul Jung, and Sung-Hyon Myaeng. 2010. Identifying controversial issues and their sub-topics in news articles. In Proceedings of the Pacific-Asia Workshop on Intelligence and Security Informatics. Springer, 140--153. Google ScholarDigital Library
- Mauro Coletto, Kiran Garimella, Aristides Gionis, and Claudio Lucchese. 2017. A motif-based approach for identifying controversy. In Proceedings of the 10th International on Conference on Web and Social Media. AAAI.Google Scholar
- Michael Conover, Jacob Ratkiewicz, Matthew Francisco, Bruno Gonçalves, Filippo Menczer, and Alessandro Flammini. 2011. Political polarization on twitter. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM’11).Google Scholar
- Lincoln Dahlberg. 2007. Rethinking the fragmentation of the cyberpublic: From consensus to contestation. New Media Soc. 9, 5 (2007), 827--847.Google ScholarCross Ref
- David L. Davies and Donald W. Bouldin. 1979. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1, 2 (1979), 224--227. Google ScholarDigital Library
- Michael Dimock, Carroll Doherty, Jocelyn Kiley, and Russ Oates. 2014. Political polarization in the American public: How increasing ideological uniformity and partisan antipathy affect politics, compromise and everyday life. Pew Research Center, Washington, DC (2014).Google Scholar
- Shiri Dori-Hacohen and James Allan. 2015. Automated controversy detection on the web. In Proceedings of the European Conference on Information Retrieval. Springer, Berlin, 423--434.Google ScholarCross Ref
- Abraham Doris-Down, Husayn Versee, and Eric Gilbert. 2013. Political blend: An application designed to bring people together based on political differences. In Proceedings of the Conference on Communities and Technologies (C8T’13). 120--130. Google ScholarDigital Library
- Kevin M. Esterling, Archon Fung, and Taeku Lee. 2015. How much disagreement is good for democratic deliberation? Political Communication 32, 4 (2015), 529--551.Google ScholarCross Ref
- Wei Feng, Jiawei Han, Jianyong Wang, Charu Aggarwal, and Jianbin Huang. 2015. STREAMCUBE: Hierarchical spatio-temporal hashtag clustering for event exploration over the twitter stream. In Proceedings of the IEEE International Conference on Data Engineering (ICDE’15).Google ScholarCross Ref
- Seth R. Flaxman, Sharad Goel, and Justin M. Rao. 2016. Filter Bubbles, Echo Chambers, and Online News Consumption. Public Opinion Quarterly 80, S1 (2016), 298--320.Google ScholarCross Ref
- Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. 2016. Exploring controversy in twitter. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW’16). 33--36. Google ScholarDigital Library
- Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. 2016. Quantifying controversy in social media. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM’16). 33--42. Google ScholarDigital Library
- Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. 2017. Factors in recommending contrarian content on social media. In Proceedings of the International ACM Web Science Conference (WebSci’17). Google ScholarDigital Library
- Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. 2017. Reducing controversy by connecting opposing views. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM’17). 81--90. Google ScholarDigital Library
- Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. 2017. The effect of collective attention on controversial debates on social media. In Proceedings of the International ACM Web Science Conference (WebSci’17). 43--52. Google ScholarDigital Library
- Kiran Garimella and Ingmar Weber. 2017. A long-term analysis of polarization on twitter. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’17).Google Scholar
- Eduardo Graells-Garrido, Mounia Lalmas, and Daniele Quercia. 2016. Data portraits and intermediary topics: Encouraging exploration of politically diverse profiles. In Proceedings of the 21st International Conference on Intelligent User Interfaces. ACM, 228--240. Google ScholarDigital Library
- Catherine Grevet, Loren G. Terveen, and Eric Gilbert. 2014. Managing political differences in social media. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW’14). 1400--1408. Google ScholarDigital Library
- Pedro Henrique Calais Guerra, Wagner Meira Jr, Claire Cardie, and Robert Kleinberg. 2013. A measure of polarization on social media networks based on community boundaries. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’13).Google Scholar
- Mathieu Jacomy, Tommaso Venturini, Sebastien Heymann, and Mathieu Bastian. 2014. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PloS one 9, 6 (2014), e98679.Google ScholarCross Ref
- Myungha Jang, John Foley, Shiri Dori-Hacohen, and James Allan. 2016. Probabilistic approaches to controversy detection. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2069--2072. Google ScholarDigital Library
- Glen Jeh and Jennifer Widom. 2002. SimRank: A measure of structural-context similarity. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD’02). 538--543. Google ScholarDigital Library
- George Karypis. 2002. CLUTO—A clustering toolkit. (2002).Google Scholar
- George Karypis and Vipin Kumar. 1998. A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices. University of Minnesota, Department of Computer Science and Engineering, Army HPC Research Center, Minneapolis, MN (1998).Google Scholar
- Manfred Klenner, Michael Amsler, Nora Hollenstein, and Gertrud Faaß. 2014. Verb polarity frames: A new resource and its application in target-specific polarity classification. In Proceedings of the Conference on Natural Language Processing (KONVENS’14). 106--115.Google Scholar
- Juhi Kulshrestha, Muhammad Bilal Zafar, Lisette Espin Noboa, Krishna P. Gummadi, and Saptarshi Ghosh. 2015. Characterizing information diets of social media users. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’15).Google Scholar
- Michael LaCour. 2012. A balanced news diet, not selective exposure: Evidence from a direct measure of media exposure. Midwest Political Science Association (2012).Google Scholar
- J. Richard Landis and Gary G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics 79 (1977), 159--174.Google ScholarCross Ref
- Haokai Lu, James Caverlee, and Wei Niu. 2015. BiasWatch: A lightweight system for discovering and tracking topic-sensitive opinion bias in social media. In Proceedings of the Conference on Information and Knowledge Management (CIKM’15). 213--222. Google ScholarDigital Library
- Ujjwal Maulik and Sanghamitra Bandyopadhyay. 2002. Performance evaluation of some clustering algorithms and validity indices. IEEE Trans. Parallel Anal. Mach. Intell. 24, 12 (2002), 1650--1654. Google ScholarDigital Library
- Yelena Mejova, Amy X. Zhang, Nicholas Diakopoulos, and Carlos Castillo. 2014. Controversy and sentiment in online news. CJ’14: Computation+Journalism Symposium (2014).Google Scholar
- A. J. Morales, J. Borondo, J. C. Losada, and R. M. Benito. 2015. Measuring political polarization: Twitter shows the two sides of Venezuela. Chaos: An Interdisciplinary Journal of Nonlinear Science 25, 3 (2015), 033114.Google ScholarCross Ref
- Sean A. Munson, Stephanie Y. Lee, and Paul Resnick. 2013. Encouraging reading of diverse political viewpoints with a browser widget. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’13).Google Scholar
- Diana C. Mutz. 2002. The consequences of cross-cutting networks for political participation. American Journal of Political Science 9, (2002), 838--855.Google Scholar
- Andreas Noack. 2009. Modularity clustering is force-directed layout. Phys. Rev. E 79, 2 (2009), 026102.Google ScholarCross Ref
- Eli Pariser. 2011. The Filter Bubble: What the Internet Is Hiding from You. Penguin, UK. Google ScholarDigital Library
- Ana-Maria Popescu and Marco Pennacchiotti. 2010. Detecting controversial events from twitter. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. ACM, 1873--1876. Google ScholarDigital Library
- Yiye Ruan, David Fuhry, and Srinivasan Parthasarathy. 2013. Efficient community detection in large networks using content and links. In Proceedings of the International World Wide Web Conference (WWW’13). 1089--1098. Google ScholarDigital Library
- Cass R. Sunstein. 2009. Republic.com 2.0. Princeton University Press, Princeton, NJ. Google ScholarDigital Library
- Mike Thelwall. 2013. Heart and soul: Sentiment strength detection in the social web with SentiStrength. In CyberEmotions. 1--14.Google Scholar
- Mikalai Tsytsarau, Themis Palpanas, and Kerstin Denecke. 2011. Scalable detection of sentiment-based contradictions. DiversiWeb, WWW 11 (2011), 105--112.Google Scholar
- Wayne Zachary. 1977. An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33 (1977), 452--473.Google ScholarCross Ref
Index Terms
- Quantifying Controversy on Social Media
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