Francesco Corso, Giuseppe Russo, Francesco Pierri, Gianmarco De Francisci Morales. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Surveys provide valuable insights into public opinion and behavior, but their execution is costly and slow.Large language models (LLMs) have been proposed as a scalable, lowcost substitute for human respondents, but their outputs are often biased and yield invalid estimates.We study the interplay between synthesis methods that use LLMs to generate survey responses and rectification methods that debias population estimates, and explore how human responses are best allocated between them.Using two panel surveys with questions on nutrition, politics, and economics, we find that synthesis alone introduces substantial bias (24-86%), whereas combining it with rectification reduces bias below 5% and increases effective sample size by up to 14%.Overall, we challenge the common practice of using all human responses for fine-tuning, showing that under a fixed budget, allocating most to rectification results in more effective estimation.

Online platforms have sanctioned individuals and communities associated with ‘fringe’ movements linked to hate speech, violence, and terrorism — but can these sanctions contribute to the abandonment of these movements? Here, we investigate this question through the lens of r/exredpill, a recovery community on Reddit meant to help individuals leave movements within the Manosphere, a conglomerate of fringe Web-based movements focused on men’s issues. We conduct an observational study on the impact of sanctioning some of Reddit’s largest Manosphere communities on the activity levels and user influx of r/exredpill, the largest associated recovery subreddit. We find that banning a related radical community positively affects participation in r/exredpill in the period following the ban. Yet, quarantining the community, a softer moderation intervention, yields no such effects. We show that the effect induced by banning a radical community is stronger than for some of the widely discussed real-world events related to the Manosphere and that moderation actions against the Manosphere do not cause a spike in toxicity or malicious activity in r/exredpill. Overall, our findings suggest that content moderation acts as a deradicalization catalyst.

Journals and conferences worry that peer reviews assisted by artificial intelligence (AI), in particular, large language models (LLMs), may negatively influence the validity and fairness of the peer-review system, a cornerstone of modern science. In this work, we address this concern with a study of the prevalence and impact of AI-assisted peer reviews in the context of the 2024 International Conference on Learning Representations (ICLR), a large and prestigious machine-learning conference. Our contributions are threefold. Firstly, we obtain a lower bound for the prevalence of AI-assisted reviews at ICLR 2024 using the closed- and open-source LLM detectors, estimating that at least 15.8% of reviews were written with AI assistance. Secondly, we estimate the impact of AI-assisted reviews on submission scores. Considering pairs of reviews with different scores assigned to the same paper, we find that in 53.4% of pairs, the AI-assisted review scores higher than the human review (p = 0.002; relative difference in probability of scoring higher: +14.4% in favor of AI-assisted reviews). Thirdly, we assess the impact of receiving an AI-assisted peer review on submission acceptance. In a matched study, submissions near the acceptance threshold that received an AI-assisted peer review were 4.9 percentage points (p = 0.024) more likely to be accepted than submissions that did not. Overall, we show that AI-assisted reviews are consequential to the peer-review process and offer a discussion on future implications of current trends.

Fringe communities promoting conspiracy theories and extremist ideologies have thrived on mainstream platforms, raising questions about the mechanisms driving their growth. Here, we hypothesize and study a possible mechanism: new members may be recruited through fringe-interactions: the exchange of comments between members and non-members of fringe communities. We apply text-based causal inference techniques to study the impact of fringe-interactions on the growth of three prominent fringe communities on Reddit: r/Incel, r/GenderCritical, and r/The Donald. Our results indicate that fringe-interactions attract new members to fringe communities. Users who receive these interactions are up to 4.2 percentage points (pp) more likely to join fringe communities than similar, matched users who do not.This effect is influenced by 1) the characteristics of communities where the interaction happens (e.g., left vs. right-leaning communities) and 2) the language used in the interactions. Interactions using toxic language have a 5pp higher chance of attracting newcomers to fringe communities than non-toxic interactions. We find no effect when repeating this analysis by replacing fringe (r/Incel, r/GenderCritical, and r/The Donald) with non-fringe communities (r/climatechange, r/NBA, r/leagueoflegends), suggesting this growth mechanism is specific to fringe commu- nities. Overall, our findings suggest that curtailing fringe interactions may reduce the growth of fringe communities on mainstream platforms.

Giuseppe Russo, Christoph Gote, Laurence Brandenberger, Sophia Schlosser, Frank Schweitzer. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Online platforms face pressure to keep their communities civil and respectful. Thus, banning problematic online communities from mainstream platforms is often met with enthusiastic public reactions. However, this policy can lead users to migrate to alternative fringe platforms with lower moderation standards and may reinforce antisocial behaviors. As users of these communities often remain co-active across mainstream and fringe platforms, antisocial behaviors may spill over onto the mainstream platform. We study this possible spillover by analyzing 70,000 users from three banned communities that migrated to fringe platforms: r/The_Donald, r/GenderCritical, and r/Incels. Using a difference-in-differences design, we contrast co-active users with matched counterparts to estimate the causal effect of fringe platform participation on users' antisocial behavior on Reddit. Our results show that participating in the fringe communities increases users' toxicity on Reddit (as measured by Perspective API) and involvement with subreddits similar to the banned community---which often also breach platform norms. The effect intensifies with time and exposure to the fringe platform. In short, we find evidence for a spillover of antisocial behavior from fringe platforms onto Reddit via co-participation.