Papers by Francesco Pierri
Among Us: Language of Conspiracy Theorists on Mainstream Reddit (2026.acl-long)
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| Challenge: | Conspiracy theories are influential, alternative narratives that explain events through the actions of secretive, malevolent groups. |
| Approach: | They analyze a large-scale longitudinal dataset of over 500 million comments on reddit . they show that users exhibit distinctive linguistic patterns that enable machine learning models to distinguish them from the general population within individual communities. |
| Outcome: | The proposed model outperforms global classifiers by 17 percentage points. |
Conspiracy Theories and Where to Find Them on TikTok (2025.acl-long)
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| Challenge: | Existing studies on TikTok's potential to promote and amplify harmful content have not been conducted. |
| Approach: | They analyze a longitudinal dataset of 1.5M videos shared in the U.S. over three years and evaluate the effects of TikTok’s Creativity Program for monetization. |
| Outcome: | The proposed model achieves high precision in detecting harmful content, but its overall performance is comparable to fine-tuned traditional models such as RoBERTa. |
Can I Introduce My Boyfriend to My Grandmother? Evaluating Large Language Models Capabilities on Iranian Social Norm Classification (2025.findings-naacl)
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| Challenge: | Introducing the Iranian Social Norms dataset, a collection of 1,699 social norms, with Farsi adding linguistic complexity. |
| Approach: | They propose a collection of Iranian social norms with English translations and a novel Iranian dataset. |
| Outcome: | The Iranian Social Norms dataset is the first to be used in the Farsi language . it includes 1,699 social norms including environments, demographic features, and scope annotation, alongside English translations. |
Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly deployed in user-facing applications, raising concerns that they reflect and amplify social biases. |
| Approach: | They propose a Mandarin-specific evaluation framework to examine social identity biases in Chinese LLMs using Mandarin-based prompts. |
| Outcome: | The proposed framework compares ingroup (“We”) and outgroup (“They”) framings across 240 social groups salient in the Chinese context, using a two-tiered measurement framework that assesses both sentiment and toxicity. |