arXiv AI By Bernardo B. P. Medeiros (University of Florida), Malvika Jadhav (University of Florida), Allison Lu (University of Florida), Tadayoshi Kohno (Georgetown University), Vincent Bindschaedler (University of Florida), Kevin R. B. Butler (University of Florida)

It Takes a Forum: Resources & Stakeholders Sustaining the Online Synthetic Non-Consensual Intimate Image Creation Ecosystem

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The paper analyzes how malicious actors create and share synthetic non-consensual intimate images (SNCII) across online forums, focusing on 4chan and Reddit. By examining over 280,000 4chan comments and 78,000 Reddit posts, the study identifies a wide array of resources—including 156 LoRA files targeting 141 female celebrities and 54 commercial nudification apps—used by both highly technical and less sophisticated users. The findings show that knowledge transfer between experts and newcomers fuels the spread of these illicit resources, revealing gaps in current regulatory frameworks and suggesting intervention points to deter such activity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language
Sep 3

Whose Judgments Count? Representation Gaps in Crowdsourced Content Moderation Produce Unequal Protection from Perceived Toxicity

The study investigates how the demographic makeup of crowdsourced moderators influences who is protected from perceived toxic content online. Using data from 16,221 U.S. respondents who evaluated over 100,000 comments from Twitter, Reddit, and 4chan, the authors find that moderators tend to protect users who share their own demographic identities, a pattern that is amplified when moderator pools mirror the demographics of Prolific participants. Even fully representative moderator groups fail to provide equal protection, leaving Black and LGB users underprotected unless they are overrepresented.

By Zhaodi Chen, Byungkyu Lee
arXiv AI
Sep 12

Characterizing Bluesky Content Moderation Service: From Automation of Service to Landscape of Harms

The study audits Bluesky’s Moderation Service (BMS) using its 10.6 million public moderation labels from 2025. It finds that BMS operates as a human‑AI collaboration: sexual and graphic content is flagged automatically in seconds, while more nuanced or high‑stakes content requires human review that can take hours or days. The system shows high precision (0.837) but low recall (0.222), with annotators detecting 4.5 times more harmful content than the system, and clustering reveals harms ranging from hostility toward protected groups to the spread of explicit material.

By Pushpdeep Singh, Sayeh Jarollahi, Ayan Majumdar, Vabuk Pahari, Abhijnan Chakraborty, Krishna P. Gummadi, Ingmar Weber, Abhisek Dash