research-article

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Understanding Conflicts in Online Conversations

Authors:

Robert E. Kraut,

Kristen M. Altenburger,

Yi-Chia WangAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2592 - 2602

https://doi.org/10.1145/3485447.3512131

Published: 25 April 2022 Publication History

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Abstract

With the rise of social media, users from across the world are able to connect and converse with each other online. While these connections have facilitated a growth in knowledge, online discussions can also end in acrimonious conflict. Previous computational studies have focused on creating online conflict detection models from inferred labels, primarily examine disagreement but not acrimony, and do not examine the conflict’s emergence. Social science studies have investigated offline conflict, which can differ from its online form, and rarely examines its emergence. The current research aims to understand how online conflicts arise in online personal conversations. Our ground truth is a Facebook tool that allows group members to report conflict to administrators. We contrast discussions ending with a conflict report with paired non-conflict discussions from the same post. We study both user characteristics (e.g., historical user-to-user interactions) and conversation dynamics (e.g., changes in emotional intensity over the course of the conversation). We use logistic regression to identify the features that predict conflict. User characteristics such as the commenter’s gender and previous involvement in negative online activity are strong indicators of conflict. Conversational dynamics, such as an increase in person-oriented discussion, are also important signals of conflict. These results help us understand how conflicts emerge and suggest better detection models and ways to alert group administrators and members early on to mediate the conversation.

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Cited By

Healey PKhare PCastro ITyson GKaran MShekhar RMcQuistin SPerkins CPurver M(2024)Power and vulnerability: managing sensitive language in organizational communicationFrontiers in Psychology10.3389/fpsyg.2023.126642514Online publication date: 23-Feb-2024
https://doi.org/10.3389/fpsyg.2023.1266425
Etta GCinelli MMarco NAvalle MPanconesi AQuattrociocchi W(2024)A Topology-Based Approach for Predicting Toxic Outcomes on Twitter and YouTubeIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.339821911:5(4875-4885)Online publication date: Sep-2024
https://doi.org/10.1109/TNSE.2024.3398219
Jeong AChiu M(2024)The effects of politeness in shaping discourse in online debatesDistance Education10.1080/01587919.2024.2353257(1-19)Online publication date: 7-Jul-2024
https://doi.org/10.1080/01587919.2024.2353257
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Understanding Conflicts in Online Conversations
1. Applied computing

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

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Published: 25 April 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Healey PKhare PCastro ITyson GKaran MShekhar RMcQuistin SPerkins CPurver M(2024)Power and vulnerability: managing sensitive language in organizational communicationFrontiers in Psychology10.3389/fpsyg.2023.126642514Online publication date: 23-Feb-2024
https://doi.org/10.3389/fpsyg.2023.1266425
Etta GCinelli MMarco NAvalle MPanconesi AQuattrociocchi W(2024)A Topology-Based Approach for Predicting Toxic Outcomes on Twitter and YouTubeIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.339821911:5(4875-4885)Online publication date: Sep-2024
https://doi.org/10.1109/TNSE.2024.3398219
Jeong AChiu M(2024)The effects of politeness in shaping discourse in online debatesDistance Education10.1080/01587919.2024.2353257(1-19)Online publication date: 7-Jul-2024
https://doi.org/10.1080/01587919.2024.2353257
Fu CXiong YQiu JWu YMing YXuan QYu S(2024)Rumor Detection Based on Conflict and Bot FeaturesBig Data and Social Computing10.1007/978-981-97-5803-6_17(279-297)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5803-6_17
Allon GCarlstein JGur Y(undefined)Leveraging Consensus Effect to Optimize Ranking in Online Discussion BoardsSSRN Electronic Journal10.2139/ssrn.4215596
https://doi.org/10.2139/ssrn.4215596

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