Associations between Bystanders and Perpetrators of Online Hate: The Moderating Role of Toxic Online Disinhibition
Open accessAbstract
Hatred directed at members of groups due to their origin, race, gender, religion, or sexual orientation is not new, but it has taken on a new dimension in the online world. To date, very little is known about online hate among adolescents. It is also unknown how online disinhibition might influence the association between being bystanders and being perpetrators of online hate. Thus, the present study focused on examining the associations among being bystanders of online hate, being perpetrators of online hate, and the moderating role of toxic online disinhibition in the relationship between being bystanders and perpetrators of online hate. In total, 1480 students aged between 12 and 17 years old were included in this study. Results revealed positive associations between being online hate bystanders and perpetrators, regardless of whether adolescents had or had not been victims of online hate themselves. The results also showed an association between toxic online disinhibition and online hate perpetration. Further, toxic online disinhibition moderated the relationship between being bystanders of online hate and being perpetrators of online hate. Implications for prevention programs and future research are discussed.
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Implicitly, an intention to harm can be attributed to definitions that classify hate speech as a form of aggression or violence, because such an intention is a defining feature of these general terms.
“Implicitly, an intention to harm can be attributed to definitions that classify hate speech as a form of aggression or violence (Lehmann, 2019; Wachs et al., 2019; Wachs & Wright, 2018, 2019), because such an intention is a defining feature of these general terms.”
From A Systematic Review on Hate Speech among Children and Adolescents: Definitions, Prevalence, and Overlap with Related Phenomena (2023) · cites this paper as Wachs & Wright, 2018
The Introduction defines online hate to include denigration, harassment, and advocacy of violence against specific groups, which supports the connection to harmful or violent conduct. It does not classify hate speech itself as a form of aggression or violence, or state that an intention to harm is part of its definition. No page number is available in our copy. (LLM, conf=0.80)
Experimental — accuracy not yet measuredThis review found only two publications, based on two studies, that reported frequencies for exposure, victimization, and perpetration together.
“In terms of perspectives covered, this review found only two publications, based on two studies, that reported frequencies for exposure, victimization, and perpetration together (Blaya & Audrin, 2019; Wachs & Wright, 2018).”
From A Systematic Review on Hate Speech among Children and Adolescents: Definitions, Prevalence, and Overlap with Related Phenomena (2023) · cites this paper as Wachs & Wright, 2018
For those perpetrators identified, such characteristics included, for instance, prejudicial views of minorities or toxic online disinhibition.
“For those perpetrators identified, such characteristics included, for instance, prejudicial views of minorities (Blaya et al., 2020; Wachs et al., 2020) or toxic online disinhibition (Wachs & Wright, 2018).”
From A Systematic Review on Hate Speech among Children and Adolescents: Definitions, Prevalence, and Overlap with Related Phenomena (2023) · cites this paper as Wachs & Wright, 2018
The scope of the definitions even varied across publications taken from the same studies.
“The scope of the definitions even varied across publications taken from the same studies (e.g., Wachs & Wright, 2018, 2020).”
From A Systematic Review on Hate Speech among Children and Adolescents: Definitions, Prevalence, and Overlap with Related Phenomena (2023) · cites this paper as Wachs & Wright, 2018
The Introduction and §2.2 (Measures; page unavailable) give this paper’s description and survey items for online hate. Our copy does not compare definitions across publications or establish that the cited 2018 and 2020 publications use the same study; searches for shared data and differing definitions found no such passage. The claimed variation therefore cannot be verified from this source alone. (LLM, conf=0.72)
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- Authors
- Wachs, S., & Wright, M. F.
- Affiliations
- University of Potsdam (DE)
- Pennsylvania State University (US)
- Corresponding author
- Wachs, Sebastian
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2018-09-17
- Volume
- 15
- Issue
- 9
- Pages
- 2030
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC6163978
- OpenAlex
- W2890110226
- Cited by
- 150
- References
- 26
- Citations by year
- 2026: 17 · 2025: 22 · 2024: 27 · 2023: 28 · 2022: 19 · 2021: 20 · 2020: 10 · 2019: 7
- FWCI
- 11.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- publish.UP (University of Potsdam) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- publish.UP (University of Potsdam) (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft
- Universität Potsdam
- Topics
- Social Sciences › Psychology › Social Psychology › Bullying, Victimization, and Aggression
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Keywords
- online hate, adolescents, moderating effect, cyberbullying prevention
- MeSH terms
- Problem Behavior, Adolescent, Communication, Female, Germany, Hate, Humans, Male, Online Systems, Students
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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