With a Little Help From AI?! Governing Visual Hate Speech Through AI-Assisted Counter-Image Practices

Stefano Pedrazzi and Franziska Oehmer-Pedrazzi
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Stefano Pedrazzi: Department of Communication and Media Research, University of Fribourg, Switzerland
Franziska Oehmer-Pedrazzi: MILEVA Institut für Digitales und Gesellschaft, Switzerland / University of Applied Sciences of the Grisons, Switzerland

Media and Communication, 2026, vol. 14

Abstract: The attention-grabbing and memorable nature of visuals makes them a powerful vehicle for spreading hate online. As platforms increasingly reconsider or scale back content moderation efforts, user-centered interventions are gaining importance as complementary governance strategies. While prior research demonstrates the effectiveness of empathy-based textual counter speech, little is known about the role of visual counter speech in mitigating hateful imagery. This preregistered study examines the effectiveness of user-centered interventions against visual hate speech. Using an experimental design ( N = 666), we tested four interventions against a no-intervention control condition in response to hateful images targeting transgender persons: (a) empathy-based textual counter speech, (b) AI-generated generic counter images, (c) AI-generated specific counter images replicating elements of the original, and (d) platform-issued warning labels. Results show substantial variation across interventions. Empathy-based textual counter speech and AI-generated specific counter images showed the most consistent reductions in amplification-oriented behaviors, particularly forwarding. Visual interventions did not outperform textual counter speech, suggesting that effectiveness depends less on modality and more on the specificity and contextual alignment of the intervention. Furthermore, effectiveness varied with the intensity of the hate content. Interventions were most effective for humor-based, more ambiguously harmful content, whereas their impact diminished for more severe forms of hate. The consistent performance of AI-generated specific counter images further points to the potential of generative AI to provide users with a rapid, low-cost, and accessible means of producing context-sensitive counter speech.

Keywords: AI; counter image; counter speech; governance; hate image; hate speech (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cog:meanco:v14:y:2026:a:12503

DOI: 10.17645/mac.12503

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