3 papers
cs.SI2026
Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation
Nicolò Pagan, Christopher Barrie, Chris Andrew Bail +1
Large Language Models (LLMs) are increasingly deployed to curate and rank human-created content, yet the nature and structure of their biases in these tasks remains poorly understo…
cs.SI2025
Learning to Control Misinformation: a Closed-loop Approach for Misinformation Mitigation over Social Networks
Nicolo' Pagan, Andreas Philippou, Giulia De Pasquale
Modern social networks rely on recommender systems that inadvertently amplify misinformation by prioritizing engagement over content veracity. We present a control framework that m…
cs.CL2025
The Collective Turing Test: Large Language Models Can Generate Realistic Multi-User Discussions
Azza Bouleimen, Giordano De Marzo, Taehee Kim +5
Large Language Models (LLMs) offer new avenues to simulate online communities and social media. Potential applications range from testing the design of content recommendation algor…