research-article

#thyghgapp: Instagram Content Moderation and Lexical Variation in Pro-Eating Disorder Communities

Authors:

Stevie Chancellor,

Jessica Annette Pater,

Munmun De ChoudhuryAuthors Info & Claims

CSCW '16: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing

Pages 1201 - 1213

https://doi.org/10.1145/2818048.2819963

Published: 27 February 2016 Publication History

Abstract

Pro-eating disorder (pro-ED) communities on social media encourage the adoption and maintenance of disordered eating habits as acceptable alternative lifestyles rather than threats to health. In particular, the social networking site Instagram has reacted by banning searches on several pro-ED tags and issuing content advisories on others. We pre-sent the first large-scale quantitative study investigating pro-ED communities on Instagram in the aftermath of moderation -- our dataset contains 2.5M posts between 2011 and 2014. We find that the pro-ED community has adopted non-standard lexical variations of moderated tags to circumvent these restrictions. In fact, increasingly complex lexical variants have emerged over time. Communities that use lexical variants show increased participation and support of pro-ED (15-30%). Finally, the tags associated with content on these variants express more toxic, self-harm, and vulnerable content. Despite Instagram's moderation strategies, pro-ED communities are active and thriving. We discuss the effectiveness of content moderation as an intervention for communities of deviant behavior.

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cover image ACM Conferences

CSCW '16: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing

February 2016

1866 pages

ISBN:9781450335928

DOI:10.1145/2818048

General Chairs:
Darren Gergle
Northwestern University
,
Meredith Ringel Morris
Microsoft Research
,
Program Chairs:
Pernille Bjørn
University of Copenhagen
,
Joseph Konstan
University of Minnesota

Copyright © 2016 ACM.

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Published: 27 February 2016

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https://doi.org/10.31592/aeusbed.1589513
Nova FShinde APfafman RHarrison ALogan Delaney CPater J(2025)Sentiment Analysis of #Meanspo Tweets: Humans vs. Automatic ClassificationsProceedings of the ACM on Human-Computer Interaction10.1145/37012079:1(1-26)Online publication date: 10-Jan-2025
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https://doi.org/10.1016/j.ijhcs.2025.103468
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