Michael Bernstein

@mbernst.bsky.social

CS Professor at Stanford

CSCW folks, I wanted to highlight how excited and proud I am to see work from our community (dl.acm.org/doi/10.1145/..., CSCW '24 best paper winner led by @jiachenyan.bsky.social and @mlam.bsky.social) grow and expand ambition into this Science paper. CSCW has a ton to offer the world.

Michael Bernstein@mbernst.bsky.social · 8mo ago

Our new article in @science.org enables social media reranking outside of platforms' walled gardens. We add an LLM-powered reranking of highly polarizing political content into N=1256 participants' feeds. Downranking cools tensions with the opposite party—but upranking inflames them.

screenshot of the title and authors of the Science paper that are linked in the next post

This was such a cool experiment that I created a Zentropi labeler with a simplified version of the authors' Partisan Animosity criteria. Now anyone can experiment directly with using this labeler to try to reduce the temperature of affective polarization in their feeds. zentropi.ai/labelers/b30...

Reranking partisan animosity in algorithmic social media feeds alters affective polarization

Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used thi...

science.org

Michael Bernstein@mbernst.bsky.social · 8mo ago

Our new article in @science.org enables social media reranking outside of platforms' walled gardens. We add an LLM-powered reranking of highly polarizing political content into N=1256 participants' feeds. Downranking cools tensions with the opposite party—but upranking inflames them.

screenshot of the title and authors of the Science paper that are linked in the next post

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. 🧵

Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used this method to conduct a preregistered 10-day field experiment with 1256 participants on X during the 2024 US presidential campaign. Our experiment used a large language model to rerank posts that expressed antidemocratic attitudes and partisan animosity (AAPA). Decreasing or increasing AAPA exposure shifted out-party partisan animosity by more than 2 points on a 100-point feeling thermometer, with no detectable differences across party lines, providing causal evidence that exposure to AAPA content alters affective polarization. This work establishes a method to study feed algorithms without requiring platform cooperation, enabling independent evaluation of ranking interventions in naturalistic settings.

Our new article in @science.org enables social media reranking outside of platforms' walled gardens. We add an LLM-powered reranking of highly polarizing political content into N=1256 participants' feeds. Downranking cools tensions with the opposite party—but upranking inflames them.

screenshot of the title and authors of the Science paper that are linked in the next post

🚨New WP!🚨 Structured AI Dialogues Can Increase Happiness and Meaning in Life In a preregistered RCT, four psychology-grounded #AI chatbots improved well-being across several outcomes. Co-authors: Jonas Schoene, Johannes Eichstaedt, Aadesh Salecha, Sonja Lyubomirsky 🧵👇

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Thrilled to share that I’ve successfully defended my PhD dissertation and I will be joining MIT as an Assistant Professor starting Fall 2026, with a shared appointment between Sloan and EECS! I will be recruiting 1-2 PhD students this upcoming cycle. Consider applying to MIT EECS!

Should we use LLMs 🤖 to simulate human research subjects 🧑? In our new preprint, we argue sims can augment human studies to scale up social science as AI technology accelerates. We identify 5 tractable challenges and argue this is a promising and underused research method 🧪🧵 arxiv.org/abs/2504.02234

LLM Social Simulations Are a Promising Research Method

Accurate and verifiable large language model (LLM) simulations of human research subjects promise an accessible data source for understanding human behavior and training new AI systems. However, resul...

arxiv.org

This might be the most useful thing I have come across in social media - a personalized feed of academic papers filtered by your follower network! Highly recommend. #academicsky

Nikhil Garg@nkgarg.bsky.social · last yr.

*Please repost* @sjgreenwood.bsky.social and I just launched a new personalized feed (*please pin*) that we hope will become a "must use" for #academicsky. The feed shows posts about papers filtered by *your* follower network. It's become my default Bluesky experience bsky.app/profile/pape...

Book announcement: with Melissa Valentine, we are publishing "Flash Teams: Leading the Future of AI-Enhanced, On-Demand Work." It's a leadership book synthesizing a decade of Stanford research on how computing, online platforms, and AI reshape teamwork. Coming October from @mitpress.bsky.social!

Book cover of the Flash Teams book. "Flash Teams: Leading the Future of AI-enhanced, on-demand work". A lightning bolt image made out of square pixels.

It was great to see so many attendees at the seminar from Michael Bernstein on Friday. Thanks for visiting, @mbernst.bsky.social. The recording of "Generative Agents: Interactive Simulacra of Human Behavior" is available at the web page below. @kenholstein.bsky.social @bradamyers.bsky.social

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CMU Human-Computer Interaction Institute@hcii.cmu.edu · last yr.

Our next #HCIISeminarSeries talk & Sujeath Pareddy Memorial Lecture will be Michael Bernstein @mbernst.bsky.social of Stanford University. 🎙️"Generative Agents: Interactive Simulacra of Human Behavior" 📅 Feb. 28, 2025 🕜1:30pm ET 📍NSH 1305 + livestream 🔗 Details: hcii.cmu.edu/news/event/2...

Weiser (2025)—Sal awakens: she smells coffee. A few minutes ago her alarm clock had quietly asked "coffee?", and she had mumbled "OK." Her Alexa alarm clock did its best with the mumbled response: "Playing OK Go." Sal cried out: "Alexa, stop!" The assistant leered at her. "Alexa. Alexa! ALEXA!"

The move to use LLMs to enforce platform codes of conduct is going to expose all the intentional vagueness in the policies. LLMs can certainly enforce what is written in the policies, but I bet it’s going to yield a bunch of undesired changes to enforcement patterns.