Quantifying Controversy in Social Media

By Venkata Rama Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Michael Mathioudakis. Web Search and Data Mining, 2015

367 citations recorded by the source corpus.

Which topics spark the most heated debates in social media? Identifying these topics is a first step towards creating systems which pierce echo chambers. In this paper, we perform a systematic methodological study of controversy detection using social media network structure and content. Unlike previous work, rather than identifying controversy in a single hand-picked topic and use domain-specific knowledge, we focus on comparing topics in any domain. Our approach to quantifying controversy is a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic, which represents alignment of opinion among users; (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, as well as 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.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d0886aec62505bac34b87fa9d02104b4958d2e26

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Citations in the corpus

Community Interaction and Conflict on the Web (2018, background): “forms has focused primarily on controversies [3, 13, 20, 21, 45, 52] and anti-social behavior, in the form of trolling [11, 12, 42], sockpuppetry [39], harassment and cyberbullying [8, 23, 32, 35, 55, 66, 79], vandalism [43], hate speech [9, 15, 51, 56, 72], and others [17, 24, 40, 41, 44, 46, 54, 6”
Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features (2019, background): “…2010; Choi et al., 2010; Cao et al., 2015; Lourentzou et al., 2015; Chen et al., 2016; Addawood et al., 2017; Al-Ayyoub et al., 2017; Garimella et al., 2018) No, whether a conversation contained disagreement (Mishne and Glance, 2006; Yin et al., 2012; Allen et al., 2014; Wang and…”
Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship (2018, background): “For example, in the context of Twitter, clusters in retweet networks have been found to correlate with the political ideologies of Twitter users [7, 10, 15].”
SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles (2020, methodology): “However, most previous work has performed analysis at the document level only (Rashkin et al., 2017; Barr´on-Cede˜no et al., 2019a) or has analyzed the general patterns of online propaganda (Garimella et al., 2015; Chatﬁeld et al., 2015).”