Papers by Giuseppe Russo
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. |
Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectification (2026.acl-long)
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| Challenge: | Large language models (LLMs) are a cost-effective and time-consuming way to capture public opinion and behavior, but their outputs are often biased and yield invalid estimates. |
| Approach: | They propose to use large language models to generate survey responses and rectification methods that debias population estimates to find out how human responses are best allocated between them. |
| Outcome: | The proposed methods reduce bias below 5% and increase sample size by up to 14% under a fixed budget. |
Self-Recognition in Language Models (2024.findings-emnlp)
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| Challenge: | Existing models for language modeling are very capable, but depend on commercial providers to build them. |
| Approach: | They propose a model-generated security question to assess self-recognition in LMs . they find no evidence of general or consistent self-reason in any examined LM . |
| Outcome: | The proposed approach can be externally administered to keep track of frontier models as it does not require access to internal model parameters or output probabilities. |
The Pluralistic Moral Gap: Understanding Moral Judgment and Value Differences between Humans and Large Language Models (2026.eacl-long)
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| Challenge: | Existing studies have shown that Large Language Models (LLMs) are not fully aligned with human moral judgments. |
| Approach: | They propose a dataset of 1,618 real-world moral dilemmas paired with a distribution of human moral judgments consisting of a binary evaluation and a free-text rationale to examine how closely LLMs align with human moral judgements. |
| Outcome: | The proposed model reproduces human judgments only under high consensus; alignment deteriorates sharply when human disagreement increases. |
Helping a Friend or Supporting a Cause? Disentangling Active and Passive Cosponsorship in the U.S. Congress (2023.acl-long)
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| Challenge: | In the U.S. Congress, legislators can use active and passive cosponsorship to support bills. |
| Approach: | They develop an Encoder+RGCN based model that learns legislator representations from bill texts and speech transcripts and uses them to predict voting decisions. |
| Outcome: | The proposed model predicts active and passive cosponsorship with an F1-score of 0.88. |
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation (2020.findings-emnlp)
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| Challenge: | a new architecture for controlling, generating and augmenting text is being developed for supervised NLP tasks. |
| Approach: | They propose a conditional VAE architecture to control, generate, and augment text. |
| Outcome: | The proposed model shows high quality, diversity and attribute control in an ablation task. |