New paper in Science:

In a platform-independent field experiment, we show that reranking content expressing antidemocratic attitudes and partisan animosity in social media feeds alters affective polarization.

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Remember, if anyone asks: It rains here all the time. 🤫

🔔 The list of accepted workshops is LIVE on the website! Check out the program and call for contributions

icwsm.org/2026/program.h…

Introducing Simile.

Simulating human behavior is one of the most consequential and technically difficult problems of our time.

We raised $100M from Index, Hanabi, A* BCV, @karpathy @drfeifei @adamdangelo @rauchg @scottbelsky among others.

Consider submitting an ICWSM workshop proposal! It’s a great opportunity to create space for discussions around emerging research threads, new methods, or even old but exciting topics. Deadline: Jan 30.

🚨 New in Nature+Science!🚨
AI chatbots can shift voter attitudes on candidates & policies, often by 10+pp
🔹Exps in US Canada Poland & UK
🔹More “facts”→more persuasion (not psych tricks)
🔹Increasing persuasiveness reduces "fact" accuracy
🔹Right-leaning bots=more inaccurate

A new experiment using an #AI-powered browser extension to reorder feeds on X (formerly Twitter), and conducted independently of the X platform’s algorithm, shows that even small changes in exposure to hostile political content can measurably influence feelings toward opposing n days of X exposure.

The findings in Science provide direct causal evidence of the impact of algorithmically controlled post ranking on a user’s social media feed. scim.ag/48IH9mx

We hope that other researchers will use our methodology to run experiments that are longer, span multiple platforms, and extend beyond the US.

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Finally, in this work, we focused on affective polarization, but our framework for LLM-based feed ranking is general and can be applied to other outcomes, including well-being, mental health, and civic engagement.

/fin