What If Moderation Didn’t Mean Suppression? A Case for Personalized Content Transformation

Centralized content moderation paradigm both falls short and overreaches: 1) it fails to account for the subjective nature of harm, and 2) it acts with blunt suppression in response to content deemed harmful, even when such content can be salvaged. We first investigate this through formative interviews, documenting how seemingly benign content becomes harmful due to individual life experiences. Based on these insights, we developed DIY-MOD, a browser extension that operationalizes a new paradigm: personalized content transformation. Operating on a user’s own definition of harm, DIY-MOD transforms sensitive elements within content in real-time instead of suppressing the content itself. The system selects the most appropriate transformation for a piece of content from a diverse palette—from obfuscation to artistic stylizing—to match the user’s specific needs while preserving the content’s informational value. Our two user studies demonstrate that this approach increases users’ sense of agency and safety, enabling them to engage with content and communities they previously needed to avoid.

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Publication Details

Published
2026-04-13
DOI
https://doi.org/10.1145/3772318.3790495
Citations
1
Primary Topic
Natural Language Processing Techniques
Type
article
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article

What If Moderation Didn’t Mean Suppression? A Case for Personalized Content Transformation

Farnaz Jahanbakhsh
1 citations
Natural Language Processing Techniques
article

What If Moderation Didn’t Mean Suppression? A Case for Personalized Content Transformation

Farnaz Jahanbakhsh
article en
1 citations

Abstract

Centralized content moderation paradigm both falls short and overreaches: 1) it fails to account for the subjective nature of harm, and 2) it acts with blunt suppression in response to content deemed harmful, even when such content can be salvaged. We first investigate this through formative interviews, documenting how seemingly benign content becomes harmful due to individual life experiences. Based on these insights, we developed DIY-MOD, a browser extension that operationalizes a new paradigm: personalized content transformation. Operating on a user’s own definition of harm, DIY-MOD transforms sensitive elements within content in real-time instead of suppressing the content itself. The system selects the most appropriate transformation for a piece of content from a diverse palette—from obfuscation to artistic stylizing—to match the user’s specific needs while preserving the content’s informational value. Our two user studies demonstrate that this approach increases users’ sense of agency and safety, enabling them to engage with content and communities they previously needed to avoid.

University of Michigan (US)
Openalex Percentile: Top 99%
Natural Language Processing Techniques
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