Quantifying controversy in social media

Venkata Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Michael Mathioudakis

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

70 Citations (Scopus)
317 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining
PublisherACM
Pages33-42
Number of pages10
ISBN (Print)978-1-4503-3716-8
DOIs
Publication statusPublished - 8 Feb 2016
MoE publication typeA4 Article in a conference publication
EventACM International Conference on Web Search and Data Mining - San Francisco, United States
Duration: 22 Feb 201625 Feb 2016
Conference number: 9

Conference

ConferenceACM International Conference on Web Search and Data Mining
Abbreviated titleWSDM
CountryUnited States
CitySan Francisco
Period22/02/201625/02/2016

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