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Elenco Pubblicazioni - Publications (1697)

Cerulli, A., Cima, L., Tessa, B., Tardelli, S., & Cresci, S. (2026). The Big Ban Theory: A Pre-and Post-Intervention Dataset of Online Content Moderation Actions. In Proceedings of the International AAAI Conference on Web and Social Media.

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Online platforms rely on moderation interventions to curb harmful behavior such as hate speech, toxicity, and the spread of mis- and disinformation. Yet research on the effects and possible biases of such interventions faces multiple…
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Ricco, E., Onofri, E., Cima, L., Cresci, S. & Di Pietro, R. (2026). A Geometric Analysis of Small-sized Language Model Hallucinations. In Proceedings of ICML

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Hallucinations—plausible but factually incorrect responses—pose a major challenge to the reliability of Large Language Models (LLMs), especially in multi-step or agentic settings. Existing work largely frames hallucinations as a consequence of missing knowledge; we show…
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Taday, E., Cima, L., Fagni, T., Avvenuti, M. & Cresci, S. (2026). Assessing the Reliability of Persona-Conditioned LLMs as Synthetic Survey Respondents. In Companion Publication of the ACM on Web Conference.

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Using persona-conditioned LLMs as synthetic survey respondents has become a common practice in computational social science and agent-based simulations. Yet, it remains unclear whether multi-attribute persona prompting improves LLM reliability or instead introduces distortions. Here…
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Tardelli, S., Alvisi, L., Cima, L., Cresci, S., & Tesconi, M. (2026). Emoji Reactions on Telegram: Unreliable Indicators of Emotional Resonance. In 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA)

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Emoji reactions are a frequently used feature of messaging platforms, yet their communicative role remains understudied. Prior work on emojis has focused predominantly on in-text usage, showing that emojis embedded in messages tend to amplify…
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Ground-based robotic remote sensing for standardized biodiversity monitoring in coastal habitats

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Autonomous remote-sensing technologies are increasingly contributing to biodiversity monitoring by enabling scalable, repeatable, and minimally invasive data collection. We present a ground-based robotic remote-sensing framework that integrates artificial intelligence and standardized quality assurance to support…
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