Scholar
Meysam Alizadeh
Google Scholar ID: FyKst9AAAAAJ
Senior Researcher, University of Zurich
Citations & Impact
Citations
2,198
H-index
15
i10-index
18
Publications
20
Co-authors
21
list available
Publications
Resume (English only)
Academic Achievements
- Invited talk at The Future of Social Media Research Workshop, University of Oxford, November 2025
- Joined Program Committee for The Web Conference 2026 (Social Networks and Social Media track), October 2025
- Invited talk at 'What Policies Can Promote Healthy AI?' consensus event, Austin, TX, May 2024
- Invited talk at Digital Services Act Stakeholder Event, European Commission, Belgium, June 2023
- Interview with France 24 on 'Twitter: All to the Right under Elon Musk?', May 2023
- Preprints include: 'Web-Browsing LLMs Can Access Social Media Profiles and Infer User Demographics' (2025); 'Simple Prompt Injection Attacks Can Leak Personal Data Observed by LLM Agents During Task Execution' (2025); 'Unsupervised Elicitation of Moral Values from Language Models'; 'Data marketplaces can increase the willingness to share social media data at low prices' (2025); 'Open-Source Large Language Models Outperform Crowd Workers and Approach ChatGPT in Text-Annotation Tasks' (arXiv:2307.02179, 2023)
- Published 'Open-Source LLMs for Text Annotation: A Practical Guide for Model Setting and Fine-Tuning' in Journal of Computational Social Science, 2025
Background
- Senior Researcher in the Department of Political Science at the University of Zurich
- Research at the intersection of AI and computational social science (CSS)
- Focuses on emergent capabilities of LLMs in social science research, their safety & alignment issues, and interactions with democracy
- Uses LLMs (fine-tuning, red-teaming, elicitation), NLP, online experiments, and machine learning to analyze digital trace data and translate findings into actionable policy insights
- Aims to develop a robust framework for the safe and ethical integration of LLMs into CSS research workflows
