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Community Interaction and Conflict on the Web

Authors:

William L. Hamilton,

Dan JurafskyAuthors Info & Claims

WWW '18: Proceedings of the 2018 World Wide Web Conference

Pages 933 - 943

https://doi.org/10.1145/3178876.3186141

Published: 23 April 2018 Publication History

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Abstract

Users organize themselves into communities on web platforms. These communities can interact with one another, often leading to conflicts and toxic interactions. However, little is known about the mechanisms of interactions between communities and how they impact users.

Here we study intercommunity interactions across 36,000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community. We show that such conflicts tend to be initiated by a handful of communities---less than 1% of communities start 74% of conflicts. While conflicts tend to be initiated by highly active community members, they are carried out by significantly less active members. We find that conflicts are marked by formation of echo chambers, where users primarily talk to other users from their own community. In the long-term, conflicts have adverse effects and reduce the overall activity of users in the targeted communities.

Our analysis of user interactions also suggests strategies for mitigating the negative impact of conflicts---such as increasing direct engagement between attackers and defenders. Further, we accurately predict whether a conflict will occur by creating a novel LSTM model that combines graph embeddings, user, community, and text features. This model can be used to create an early-warning system for community moderators to prevent conflicts. Altogether, this work presents a data-driven view of community interactions and conflict, and paves the way towards healthier online communities.

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Cited By

Feng BCheng FLiu YChang XWang XJin D(2024)Community Detection on Social Networks With Sentimental InteractionInternational Journal on Semantic Web and Information Systems10.4018/IJSWIS.34123220:1(1-23)Online publication date: 27-Mar-2024
https://doi.org/10.4018/IJSWIS.341232
Louati ALouati HAlbanyan ALahyani RKariri EAlabduljabbar A(2024)Harnessing Machine Learning to Unveil Emotional Responses to Hateful Content on Social MediaComputers10.3390/computers1305011413:5(114)Online publication date: 29-Apr-2024
https://doi.org/10.3390/computers13050114
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https://doi.org/10.3233/IDA-230194
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Community Interaction and Conflict on the Web

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cover image ACM Other conferences

WWW '18: Proceedings of the 2018 World Wide Web Conference

April 2018

2000 pages

ISBN:9781450356398

General Chairs:
Pierre-Antoine Champin
Universitè Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

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Published: 23 April 2018

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WWW '18: The Web Conference 2018

April 23 - 27, 2018

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WWW '18 Paper Acceptance Rate 170 of 1,155 submissions, 15%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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https://doi.org/10.4018/IJSWIS.341232
Louati ALouati HAlbanyan ALahyani RKariri EAlabduljabbar A(2024)Harnessing Machine Learning to Unveil Emotional Responses to Hateful Content on Social MediaComputers10.3390/computers1305011413:5(114)Online publication date: 29-Apr-2024
https://doi.org/10.3390/computers13050114
Johnson JGiraud-Carrier C(2024)GT-CHES: Graph transformation for classification in human evolutionary systemsIntelligent Data Analysis10.3233/IDA-23019428:3(699-715)Online publication date: 28-May-2024
https://doi.org/10.3233/IDA-230194
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