research-article

Quantifying Controversy in Social Media

Authors:

Kiran Garimella,

Gianmarco De Francisci Morales,

Aristides Gionis,

Michael MathioudakisAuthors Info & Claims

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Pages 33 - 42

https://doi.org/10.1145/2835776.2835792

Published: 08 February 2016 Publication History

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.

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https://doi.org/10.35120/sciencej0302075z
Muñoz PBellogín ABarba-Rojas RDíez F(2024)Quantifying polarization in online political discourseEPJ Data Science10.1140/epjds/s13688-024-00480-313:1Online publication date: 5-Jun-2024
https://doi.org/10.1140/epjds/s13688-024-00480-3
Lenti JMejova YKalimeri KPanisson APaolotti DTizzani MStarnini M(2023)Global Misinformation Spillovers in the Vaccination Debate Before and During the COVID-19 Pandemic: Multilingual Twitter StudyJMIR Infodemiology10.2196/447143(e44714)Online publication date: 24-May-2023
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Index Terms

Quantifying Controversy in Social Media
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Internet communications tools

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cover image ACM Conferences

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

February 2016

746 pages

ISBN:9781450337168

DOI:10.1145/2835776

General Chairs:
Paul N. Bennett
Microsoft Research
,
Vanja Josifovski
Pinterest
,
Program Chairs:
Jennifer Neville
Purdue University
,
Filip Radlinski
Microsoft

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 08 February 2016

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SoBigData: Social Mining & Big Data Ecosystem

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WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining

February 22 - 25, 2016

California, San Francisco, USA

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WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

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https://doi.org/10.35120/sciencej0302075z
Muñoz PBellogín ABarba-Rojas RDíez F(2024)Quantifying polarization in online political discourseEPJ Data Science10.1140/epjds/s13688-024-00480-313:1Online publication date: 5-Jun-2024
https://doi.org/10.1140/epjds/s13688-024-00480-3
Lenti JMejova YKalimeri KPanisson APaolotti DTizzani MStarnini M(2023)Global Misinformation Spillovers in the Vaccination Debate Before and During the COVID-19 Pandemic: Multilingual Twitter StudyJMIR Infodemiology10.2196/447143(e44714)Online publication date: 24-May-2023
https://doi.org/10.2196/44714
Monti CD'Ignazi JStarnini MDe Francisci Morales G(2023)Evidence of Demographic rather than Ideological Segregation in News Discussion on RedditProceedings of the ACM Web Conference 202310.1145/3543507.3583468(2777-2786)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583468
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Folly KBoughaba YMalek M(2023)Leveraging Nodal and Topological Information for Studying the Interaction Between Two Opposite Ego NetworksSocial Computing and Social Media10.1007/978-3-031-35927-9_21(295-307)Online publication date: 9-Jul-2023
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Righi ACatanese EValentino LZardetto D(2023)The Italian Social Mood on Economy Index During the Covid-19 CrisisStudies in Theoretical and Applied Statistics10.1007/978-3-031-16609-9_29(475-485)Online publication date: 15-Feb-2023
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Badache IChifu AFournier S(2022)Unsupervised and Supervised Methods to Estimate Temporal-Aware Contradictions in Online Course ReviewsMathematics10.3390/math1005080910:5(809)Online publication date: 3-Mar-2022
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Mejova YAn JMorales GKwak H(2022)Modeling Political Activism around Gun Debate via Social MediaACM Transactions on Social Computing10.1145/35321025:1-4(1-28)Online publication date: 26-Nov-2022
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Interian RG. Marzo RMendoza IRibeiro C(2022)Network polarization, filter bubbles, and echo chambers: an annotated review of measures and reduction methodsInternational Transactions in Operational Research10.1111/itor.1322430:6(3122-3158)Online publication date: 21-Oct-2022
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