Caught in the storm: A qualitative study on digital hate targeting scholars

Social media has become a platform where scholars experience digital hate targeting both their research and personal lives. Extending the Transactional Model of Stress and Coping (TMSC) to address the unique challenges of fast-evolving digital hate victimization (DHV), we draw on 24 qualitative interviews with scholars from Austria, France, Hungary, and Sweden. The study explores hateful communication, scholars’ understanding of digital hate, its consequences, and their coping strategies. Our findings reveal that scholars frequently face qualitatively distinct forms of digital hate simultaneously, including incivility, intolerance, and threats, delivered in overlapping and cumulative forms, often in overwhelming quantities, leading to both short- and long-term psychological and professional consequences. Consequently, many resort to reducing their social media presence and public visibility as a coping strategy, which, in turn, diminishes their willingness to engage in science communication and interact with the public. These findings underscore the urgent need for stronger institutional and policy measures to safeguard scholars’ well-being and protect academic freedom in digitally mediated public spheres. • Scholars often face incivility, intolerance, and threats simultaneously. • Digital hate impacts scholars’ well-being and limits science communication. • Coping with digital hate is non-linear and multifaceted, challenging linear stress models. • TMSC is extended to reflect fast-changing digital hate dynamics. • Countermeasures must reflect digital hate’s heterogeneity and non-sequential coping responses.

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Publication Details

Journal
Computers in Human Behavior Reports
Published
2026-04-06
DOI
https://doi.org/10.1016/j.chbr.2026.101059
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

Caught in the storm: A qualitative study on digital hate targeting scholars

Kevin Koban, Maryam Khaleghipour, Jörg Matthes
Computers in Human Behavior Reports
Hate Speech and Cyberbullying Detection
article

Caught in the storm: A qualitative study on digital hate targeting scholars

Kevin Koban, Maryam Khaleghipour, Jörg Matthes
article en

Abstract

Social media has become a platform where scholars experience digital hate targeting both their research and personal lives. Extending the Transactional Model of Stress and Coping (TMSC) to address the unique challenges of fast-evolving digital hate victimization (DHV), we draw on 24 qualitative interviews with scholars from Austria, France, Hungary, and Sweden. The study explores hateful communication, scholars’ understanding of digital hate, its consequences, and their coping strategies. Our findings reveal that scholars frequently face qualitatively distinct forms of digital hate simultaneously, including incivility, intolerance, and threats, delivered in overlapping and cumulative forms, often in overwhelming quantities, leading to both short- and long-term psychological and professional consequences. Consequently, many resort to reducing their social media presence and public visibility as a coping strategy, which, in turn, diminishes their willingness to engage in science communication and interact with the public. These findings underscore the urgent need for stronger institutional and policy measures to safeguard scholars’ well-being and protect academic freedom in digitally mediated public spheres. • Scholars often face incivility, intolerance, and threats simultaneously. • Digital hate impacts scholars’ well-being and limits science communication. • Coping with digital hate is non-linear and multifaceted, challenging linear stress models. • TMSC is extended to reflect fast-changing digital hate dynamics. • Countermeasures must reflect digital hate’s heterogeneity and non-sequential coping responses.

Computers in Human Behavior ReportsVol. 22
University of Vienna (AT)
European Research Council
Openalex Percentile: Top 25%
Hate Speech and Cyberbullying Detection
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