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Detecting Toxicity Triggers in Online Discussions

Authors:

Hind Almerekhi,

Haewoon Kwak,

Bernard J. Jansen,

Joni SalminenAuthors Info & Claims

HT '19: Proceedings of the 30th ACM Conference on Hypertext and Social Media

Pages 291 - 292

https://doi.org/10.1145/3342220.3344933

Published: 12 September 2019 Publication History

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Abstract

Despite the considerable interest in the detection of toxic comments, there has been little research investigating the causes -- i.e., triggers -- of toxicity. In this work, we first propose a formal definition of triggers of toxicity in online communities. We proceed to build an LSTM neural network model using textual features of comments, and then, based on a comprehensive review of previous literature, we incorporate topical and sentiment shift in interactions as features. Our model achieves an average accuracy of 82.5% of detecting toxicity triggers from diverse Reddit communities.

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Cited By

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Nourollahi SBaradaran RAmirkhani H(2024)Domain adaptation-based method for improving generalization of hate speech detection modelsSignal and Data Processing10.61186/jsdp.21.1.12521:1(125-142)Online publication date: 1-Jun-2024
https://doi.org/10.61186/jsdp.21.1.125
Karhu MRantasila AOlsson THäkkilä J(2024)Understanding animal-oriented social media collaboration in Australia’s 2019–20 bushfire crisisFrontiers in Communication10.3389/fcomm.2024.14403119Online publication date: 12-Sep-2024
https://doi.org/10.3389/fcomm.2024.1440311
Smith CKlassen SPrinster GTan CKeegan B(2024)Governance of the Black Experience on Reddit: r/BlackPeopleTwitter as a Case Study in Supporting Sense of Virtual Community for Black UsersProceedings of the ACM on Human-Computer Interaction10.1145/36869208:CSCW2(1-32)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686920
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Index Terms

Detecting Toxicity Triggers in Online Discussions
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social media

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Information & Contributors

Information

Published In

HT '19: Proceedings of the 30th ACM Conference on Hypertext and Social Media

September 2019

326 pages

ISBN:9781450368858

DOI:10.1145/3342220

General Chairs:
Claus Atzenbeck
Hof University, Germany
,
Jessica Rubart
OWL University of Applied Sciences and Arts, Germany
,
Program Chair:
David E. Millard
University of Southampton, UK

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2019

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HT '19

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HT '19: 30th ACM Conference on Hypertext and Social Media

September 17 - 20, 2019

Hof, Germany

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HT '19 Paper Acceptance Rate 20 of 68 submissions, 29%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

View all

Nourollahi SBaradaran RAmirkhani H(2024)Domain adaptation-based method for improving generalization of hate speech detection modelsSignal and Data Processing10.61186/jsdp.21.1.12521:1(125-142)Online publication date: 1-Jun-2024
https://doi.org/10.61186/jsdp.21.1.125
Karhu MRantasila AOlsson THäkkilä J(2024)Understanding animal-oriented social media collaboration in Australia’s 2019–20 bushfire crisisFrontiers in Communication10.3389/fcomm.2024.14403119Online publication date: 12-Sep-2024
https://doi.org/10.3389/fcomm.2024.1440311
Smith CKlassen SPrinster GTan CKeegan B(2024)Governance of the Black Experience on Reddit: r/BlackPeopleTwitter as a Case Study in Supporting Sense of Virtual Community for Black UsersProceedings of the ACM on Human-Computer Interaction10.1145/36869208:CSCW2(1-32)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686920
Laato SKordyaka BHamari J(2024)Traumatizing or Just Annoying? Unveiling the Spectrum of Gamer Toxicity in the StarCraft II CommunityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642137(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642137
Kiddle RTörnberg PTrilling D(2024)Network toxicity analysis: an information-theoretic approach to studying the social dynamics of online toxicityJournal of Computational Social Science10.1007/s42001-023-00239-27:1(305-330)Online publication date: 1-Feb-2024
https://doi.org/10.1007/s42001-023-00239-2
Klein MMajdoubi N(2024)The medium is the message: toxicity declines in structured vs unstructured online deliberationsWorld Wide Web10.1007/s11280-024-01269-027:3Online publication date: 8-May-2024
https://dl.acm.org/doi/10.1007/s11280-024-01269-0
Johnson RAl-khateeb SForbes ACupido K(2023)Targeted Social Media Harassment: A Comparative Analysis of Toxicity Directed at Men and Women Sports ReportersCommunication & Sport10.1177/2167479523121333012:3(443-465)Online publication date: 9-Nov-2023
https://doi.org/10.1177/21674795231213330
Nascimento FCavalcanti GDa Costa-Abreu M(2023)Exploring Automatic Hate Speech Detection on Social Media: A Focus on Content-Based AnalysisSage Open10.1177/2158244023118131113:2Online publication date: 17-Jun-2023
https://doi.org/10.1177/21582440231181311
Ashish Rani AShyan H(2023)A Comparative Study and Analysis on Toxic Comment Classification2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)10.1109/ICSCSS57650.2023.10169771(783-787)Online publication date: 14-Jun-2023
https://doi.org/10.1109/ICSCSS57650.2023.10169771
Ravi KVela AJenaway EWindisch S(2023)Exploring Multi-Level Threats in Telegram Data with AI-Human Annotation: A Preliminary Study2023 International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA58977.2023.00229(1520-1527)Online publication date: 15-Dec-2023
https://doi.org/10.1109/ICMLA58977.2023.00229
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Investigating Toxicity Across Multiple Reddit Communities, Users, and Moderators

Exploring Antecedents and Consequences of Toxicity in Online Discussions: A Case Study on Reddit

Are These Comments Triggering? Predicting Triggers of Toxicity in Online Discussions