Morally Motivated Networked Harassment as Normative Reinforcement
Open accessAbstract
While online harassment is recognized as a significant problem, most scholarship focuses on descriptions of harassment and its effects. We lack explanations of why people engage in online harassment beyond simple bias or dislike. This article puts forth an explanatory model where networked harassment on social media functions as a mechanism to enforce social order. Drawing from examples of networked harassment taken from qualitative interviews with people who have experienced harassment ( n = 28) and Trust & Safety workers at social platforms ( n = 9), the article builds on Brady, Crockett, and Bavel’s model of moral contagion to explore how moral outrage is used to justify networked harassment on social media. In morally motivated networked harassment, a member of a social network or online community accuses a target of violating their network’s norms, triggering moral outrage. Network members send harassing messages to the target, reinforcing their adherence to the norm and signaling network membership. Frequently, harassment results in the accused self-censoring and thus regulates speech on social media. Neither platforms nor legal regulations protect against this form of harassment. This model explains why people participate in networked harassment and suggests possible interventions to decrease its prevalence.
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Sanctioning political speech on social media is driven by partisan norms and identity signaling
Ahn, C., Yphtach Lelkes, B., & Levendusky, M. S. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae534 · Cited by 4 · see citing works
Misinformation as strategy: Epistemic consequences and the undermining of shared truth
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Morales, E. (2023) · Investigación & Desarrollo · doi:10.14482/indes.31.02.814.456 · Cited by 4 · see citing works
Kosonen, H. S., & Löf, R. (2024) · European Journal of Cultural Studies · doi:10.1177/13675494241278931 · Cited by 4 · see citing works
Dogwhistles, Discrimination, Humour and the Law: Regulating Implicit Messaging
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“Why can’t I just be fat and exist on the internet?”: The embeddedness of antifatness on TikTok
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Corporate social advocacy events as a window into the contemporary promotional industries
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The violence of online conspiracy theories
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Digital recording and the hazards of unbounded moralized judgment
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Structural failures of care: institutional disregard for researcher safety online
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Justice court versus social media court
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Evaluating the Social Media Profiles of Online Harassers
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- Authors
- Marwick, A. E.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Marwick, Alice E.
- ORCID
- Marwick, Alice E.
- Venue
- Social Media + Society
- Publisher
- SAGE Publications
- Published
- 2021-04
- Volume
- 7
- Issue
- 2
- ISSN
- 2056-3051 (ISSN-L)
- OpenAlex
- W3168421002
- Cited by
- 185
- References
- 51
- Citations by year
- 2026: 30 · 2025: 50 · 2024: 52 · 2023: 35 · 2022: 14 · 2021: 4
- FWCI
- 9.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Social Media + Society (publishedVersion, cc-by)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UNC Libraries
- Funders
- Data & Society Research Institute (Fellowship)
- Institute for the Arts and Humanities at UNC Chapel Hill (Faculty Fellowship)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Stalking, Cyberstalking, and Harassment
- Social Sciences › Sociology and Political Science › Workplace Violence and Bullying
- Keywords
- online harassment, moral contagion, moral outrage, social norms, social order, self-censorship, speech regulation, social networking sites, online communities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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