Meysam Alizadeh

Senior Researcher

University of Zurich

Welcome!

I am a Senior Researcher in the Department of Political Science at the University of Zurich. Prior to this, I held postdoctoral appointments at Harvard University, Princeton University, and Indiana University. I obtained my PhD in Computational Social Science from George Mason University, where I was affiliated with the Social Complexity Lab and served as a visiting researcher at the Social Dynamics Lab at Cornell University. I also hold an MSc in Information Science from the University of Pittsburgh, with additional research affiliation at the Agent-Robotics Technology Lab at Carnegie Mellon University.

My research sits at the intersection of AI, Computational Social Science, and Governance. My work develops frameworks for evaluating emergent capabilities, safety, and alignment in LLMs and investigates the broader sociotechnical conditions that shape their safe and ethical integration into society.

Download my CV .

Interests

Computational Social Science
LLM
Privacy
AI Safety & Alignment

Education

Ph.D. in Computational Social Science, 2016

George Mason University
M.Sc. in Information Science, 2012

University of Pittsburgh
M.Sc. in Industrial Engineering, 2009

Amirkabir University of Technology
B.Sc. in Industrial Engineering, 2007

Amirkabir University of Technology

News

November 2025 — Invited talk at The Future of Social Media Research Workshop, University of Oxford.
October 2025 — Joined the Program Committee for The Web Conference 2026, Research Track on Social Networks and Social Media.
May 2024 — Invited talk at the workshop What Policies Can Promote Healthy AI? (Consensus Event, Austin, TX).
June 2023 — Invited talk at the Digital Services Act Stakeholder Event, European Commission, Belgium.
May 2023 — Interview with France 24 on “Twitter: All to the Right under Elon Musk?”.

Ongoing Research

Work in Progress

Meysam Alizadeh “Adversarial LLM Search Engine Optimization on Social Media”.
Meysam Alizadeh “Benchmarking LLM Agents Authentication and Authorization Protocols Against Adversarial Attacks”.

Pre-Prints

Meysam Alizadeh, Fabrizio Gilardi, Zeinab Samei and Mohsen Mosleh (2025) Web-Browsing LLMs Can Access Social Media Profiles and Infer User Demographics
Meysam Alizadeh, Zeynab Samei, Daria Stetsenko, Fabrizio Gilardi (2025) “Simple Prompt Injection Attacks Can Leak Personal Data Observed by LLM Agents During Task Execution”
Meysam Alizadeh, Mael Kubli and Fabrizio Gilardi “Unsupervised Elicitation of Moral Values from Language Models”.
MeysamAlizadeh and Fabrizio Gilardi (2025) Data marketplaces can increase the willingness to share social media data at low prices
Meysam Alizadeh, Mael Kubli, Zeinab Samei, Shirin Dehghani, Juan Diego Bermeo, Maria Ko robeynikova, & Fanrizio Gilardi (2023) “Open-Source Large Language Models Outperform Crowd Workers and Approach ChatGPT in Text-Annotation Tasks”. arXiv preprint arXiv:2307.02179.

Publications

Meysam Alizadeh, Mael Kubli, Zeinab Samei, Shirin Dehghani, Juan Diego Bermeo, Maria Korobeynikova, Fabrizio Gilardi (2025). Open-Source LLMs for Text Annotation: A Practical Guide for Model Setting and Fine-Tuning. Journal of Computational Social Science, 8(1), 1–25.

Nahema Marchal, Emma Hoes, K Jonathan Klüser, Felix Hamborg, Meysam Alizadeh, Mael Kubli, Christian Katzenbach (2025). How Negative Media Coverage Impacts Platform Governance: Evidence from Facebook, Twitter, and YouTube. Political Communication, 42(2), 1–19.

Meysam Alizadeh, Darya Zare, Zeynab Samei, Mohammadamin Alizadeh, Mael Kubli, Mohammadhadi Aliahmadi, Sarvenaz Ebrahimi, Fabrizio Gilardi (2024). Comparing methods for creating a national random sample of Twitter users. Social Network Analysis and Mining, 14(160).

Fabrizio Gilardi, Meysam Alizadeh, Mael Kubli (2023). ChatGPT outperforms crowd-workers for text-annotation tasks. PNAS, 120(30), e2305016120.

Meysam Alizadeh, Emma Hoes, Fabrizio Gilardi (2023). Monetization of social media engagements increases the sharing of false (and other) news but penalization moderates it. Scientific Reports, 13(1), 13703.

Meysam Alizadeh, Emma Hoes, Nahema Marchal, Mael Kubli, Jonathan Kaluser, Fabrizio Gilardi (2022). Content Moderation as a Political Issue: The Twitter Discourse Around Trump’s Ban. Journal of Quantitative Description: Digital Media, 2.

Veronika Batzdorfer, Holger Steinmetz, Marco Biella, Meysam Alizadeh (2022). Conspiracy theories on Twitter: emerging motifs and temporal dynamics during the COVID-19 pandemic. International Journal of Data Science and Analytics, 13, 315–333.

Meysam Alizadeh, Jacob Shapiro, Cody Buntain, Joshua Tucker (2020). Content-based features predict social media influence operations. Science Advances, 6(3), eabb5824.

Meysam Alizadeh, Ingmar Weber, Claudio Cioffi-Revilla, Santo Fortunato, Michael Macy (2019). Psychology and Morality of Political Extremists: Evidence from Twitter Language Analysis of Alt-Right and Antifa. EPJ Data Science, 8:17.

Meysam Alizadeh, Claudio Cioffi-Revilla, Andrew Crooks (2017). Generating Spatial Social Networks. Computational and Mathematical Organization Theory, 23(3), 362–390.

Mohsen Gharakhani, Fatemeh Nasiri, Meysam Alizadeh (2016). A Utility Theory Approach for Insurance Pricing. Accounting, 2(4), 151–160.

Teaching

University of Zurich

Digital Public Governance, BA/MA Seminar, Spring 2026.

University of Maryland Baltimore County

Introduction to Data Analysis, BA/MA Seminar, Spring 2010.

University of Tehran

Curriculum Design: Developed the full curriculum for M.A. and Ph.D. programs in Computational Social Science, Department of Computer Engineering, 2022.
Introduction to Computational Social Science, Lecture, Fall 2020.

Institute for Research in Fundamental Sciences

Workshop: Social Data Analysis, Spring 2022.

Sharif University of Technology

Workshop: AI in Social Media Research, Summer 2021.

Supervision

Dissertation, thesis, and project committees: Aida Mahmoudi (ICSS), Savana Diem (UZH), Hadi Aliahmadi (IUST), Mohammad Ghotbi (UT), Fatemeh Rajabi (AUT), Mahdi Davoudi (IRIBU), Ata Naghedifar (SUT), Masiha Zahedivafa (IUST).
Mentorship: Henrietta Toivanen (Princeton), Ali Ghazinejad (IU), Darya Zare (AUT), Alireza Mohammadian (SUT), Parsa Mirtaheri (SUT), Shirin Dehghani (ATU).

Statistics for CSS

Week 1: SHELL SCRIPTING

Shell Scripting Tutorial
Week 2: PYTHON

1. NYU Data Bootcamp
2. “Introduction to Python for Science”
3. Getting started with Jupyter notebooks
4. NumPy quickstart
5. Pandas getting started
Week 3: R

1. Simon Ejdemyr, “R Tutorials: Basics”
2. Hadley Wickham and Garrett Grolemund, “R for Data Science”
3. Getting started with R Markdown
4. R Packages
5. Top 50 ggplot2 Visualizations
Week 4: DATA CLEANING, DESCRIPTIVE ANALYSIS, AND VISUALIZATIONS

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1. World Bank’s Development Impact Evaluation (DIME):
– Tidying Data
– Data Cleaning
– Data Construction (includes constructing panel data)
2. “Missing-data imputation”
3. Jason Osborne, “Notes on the use of data transformations”,Practical Assessment, Research, and Evaluation
4.“Statistical Data Types” chapter of Andrius Buteikis, Practical Econometrics and Data Science
5. World Bank DIME, Descriptive Analysis in R
6. J-PAL, “Data Visualization”
7. United Nations’ guide to making data meaningful
8. R Graph Gallery (useful for getting ideas and example code in R) 9.Kieran Healy,Data Visualization: A Practical Introduction
9. Kieran Healy,Data Visualization: A Practical Introduction
Week 5: PROBABILITY AND REGRESSIONS

1. Chapter 2 and 3 of Scott Cunningham’s Causal Inference: The Mixtape
2. Regression and Other Stories, Gelman et al, 2022
3. “Binary Choice Models”
4. UCLA Statistical Consulting Group’s introduction to interaction terms Decomposing, Probing, and Plotting Interactions in R/Stata
5. Probability cheatsheets
Week 6: CAUSAL INFERENCE

1. Josh Angrists and Victor Chernozhukov, “Applied Econometrics: Mostly Harmless Big Data” (companion course to Mostly Harmless Econometrics)
2. MIT 14.771/ Harvard 2390b “Empirical Methods” handout
3. Chapters 4 and 5 of Scott Cunningham’s Causal Inference: The Mixtape
4. Gary King’s slides on Matching Methods for Causal Inference
Week 7: REGRESSION DISCONTINUITY & INSTRUMENTAL VARIABLES

1. Chapters 6 and 7 of Scott Cunningham’s Causal Inference: The Mixtape
Week 8: FIXED EFFECTS, DIFFERENCE-IN-DIFFERENCES

1. Chapters 8 and 9 of Scott Cunningham’s Causal Inference: The Mixtape
2. Josh Blumenstock’s lecture notes on fixed effects models
3. Fixed and Random Effects Models in Python
Week 9: STANDARD ERRORS

1. On non-standard errors:
– Summary of Athey, Abadie, Imbens and Wooldridge (2017) (full paper here)
– Nonstandard errors in code
2. Intro to power analysis

3. Thomas Brambor, William Roberts Clark and Matt Golder, “Understanding Interaction Models: Imroving Empirical Analyses”,Political Analysis14(1):2006.
Week 10: TEXT-AS-DATA (NLP)

1. Introductory Online Courses:
– Chris Bail’s Text as Data course
– Introduction to NLP in Python, Sentiment Analysis in Python, and Advanced NLP with spaCy

2. Online Open Access Book:
– Speech and Language Processing (3^rd Edition), Jurafsky and Martin

3. Data Collection
– Chapter 17 (Collecting Data from the Web) of Rochelle Terman’s PLSC 31101: Computational Tools for Social Science course notes
– Different text file formats
– Working with APIs
– HTML and CSS

4. Topic Modeling
– David M. Blei, “Surveying a suite of algorithms that offer a solution to managing large document archives,”Communications of the ACM55(4):2012
– Chris Bail’s Text as Data Course, Topic Modeling

5. Free Seminars:
– NLP + CSS Tutorials

6. Useful R and Python packages:
– Interactive LDA Visualization
– Structural Topic Modeling
– Textnets, R package for automated text analysis using network techniques
– Keyword Assisted Topic Models

7. Other Useful Resources:
– Learningregex
– regexcheatsheet
– Glossary of Linguistics Terms
Week 11: NETWORK ANALYSIS

1. Interactive Introduction to Network Science
2. Aaron Clauset’s course, A Brief Introduction to Network Science
3. Brian Keegan’s Network Science course slides
4. Carlos Castillo’s Network Science course slides
5. Creating, displaying, and sharing interactive network visualizations on the

Outreach

Mention in outlet Media

AI Solutions Seek To Advance Real World Asset Tokenization , CryptoNews, 9 May 2024.
Tokenization News Roundup: Resource Extraction, Social Media Monetization and Real World Connections , Yahoo! Finance, 24 August 2023.
Twitter: all right under Elon Musk? , France24, 25 May 2023.
ChatGPT is coming for your jobs – the terrible ones, at least , The Register, 3 April 2023.
ChatGPT outperforms humans at labelling some data for other AIs , New Scientist, 29 March 2023.
ChatGPT Can Replace the Underpaid Workers Who Train AI, Researchers Say , VICE, 29 March 2023.
Tracking the trolls , BBC Digital Planet, 29 July 2020.
A weather forecast for fake news outbreaks on social media is coming , New Scientist, 29 July 2020.
KI-System erkennt Posts von Trollen und Bots , Bild Der Wissenschaft, 27 July 2020.
Russland-Affäre: Algorithmus erkennt Trolle in staatlichem Auftrag , Deutschlandfunk Forschung Aktuell, 23 July 2020.
AI system detects posts by foreign ‘trolls’ on Facebook and Twitter , The Guardian, 22 July 2020.
Chinese and Russian Trolls Trying to Influence 2020 Election Could Be Hunted with AI Tool , Newsweek, 22 July 2020.
Tracking Disinformation Using Digital Fingerprints , Inverse, 22 July 2020.

Invited Talks

Workshop on The Future of Social Media Research, Oxford Internet Institute, 2025.
Transversal Research Priorities Areas Keynote Series, University of Zurich, 2024.
Workshop on What Policies Can Promote Healthy AI?, Consensus Event, USA, 2024.
Workshop on Safeguarding Democracy from Online Disinformation, Digital Services Act Stakeholder Event, European Commission, Belgium, 2023.
Microsoft Seminar Series, Microsoft, WA, 2020.
Center for Information Technology Policy Seminar Series, Princeton, 2020.
Center for Social Media and Politics, NYU, 2019.

Contacts

Office:
Digital Democracy Lab
Department of Political Science
University of Zurich
Affolternstrasse 56, AFL-H-353
8050 Zürich, Switzerland