TitleQuantifying Controversy in Social Media
Publication TypeConference Paper
Year of Publication2016
AuthorsGarimella, K, Morales, GDe Francis, Gionis, A, Mathioudakis, M
Conference NameProceedings of the Ninth ACM International Conference on Web Search and Data Mining
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-3716-8
Keywordscontroversy, random walks, social media, twitter
Abstract

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.

URLhttp://doi.acm.org/10.1145/2835776.2835792
DOI10.1145/2835776.2835792