Work

Content-Agnostic Moderation for Stance-Neutral Recommendations

Year: 2025

Type: conference-paper

Abstract: Personalized recommendation systems often drive users towards more extreme content, exacerbating opinion polarization. While content-aware moderation has been proposed to mitigate these effects, such approaches risk curtailing the freedom of speech and information. To address this concern, we propose and explore the feasibility of content-agnostic moderation as an alternative approach for reducing polarization. Content-agnostic moderation does not rely on the actual content being moderated, arguably making it less prone to forms of censorship. We establish theoretically that content-agnostic moderation cannot be guaranteed to work in a fully generic setting. However, we show that it can often be effectively achieved in practice with plausible assumptions. We introduce two novel content-agnostic moderation methods that modify recommendations from the content recommender to disperse user-item co-clusters without relying on content features.

Source: ACM International Conference on Information and Knowledge Management (CIKM)

DOI: 10.1145/3746252.3761111

Authors: Nan Li, Bo Kang, Tijl De Bie

Institution: Ghent University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Fonds Wetenschappelijk Onderzoek, Universiteit Gent, Vlaamse regering, HORIZON EUROPE European Research Council

Awards: G1464064545, Exploring Data: Theoretical Foundations and Applications to Web, Multimedia, and Omics Data, VIrtual GuardIan AngeLs for the post-truth Information Age