4 papers
Community Notes undermoderate polarizing content by design creating risks in electoral processes
Paul Bouchaud, Pedro Ramaciotti
Community Notes (CNs) of X enables users to collaboratively moderate misleading content. To resolve conflicting moderation, CNs infers a latent ideological dimension and selects no…
Recommender system in X inadvertently profiles ideological positions of users
Paul Bouchaud, Pedro Ramaciotti
Studies on recommendations in social media have mainly analyzed the quality of recommended items (e.g., their diversity or biases) and the impact of recommendation policies (e.g.,…
Linear socio-demographic representations emerge in Large Language Models from indirect cues
Paul Bouchaud, Pedro Ramaciotti
We investigate how LLMs encode sociodemographic attributes of human conversational partners inferred from indirect cues such as names and occupations. We show that LLMs develop lin…
Web Crawler Restrictions, AI Training Datasets \& Political Biases
Paul Bouchaud, Pedro Ramaciotti
Large language models rely on web-scraped text for training; concurrently, content creators are increasingly blocking AI crawlers to retain control over their data. We analyze craw…