Chenyan Jia
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jiachenyan.bsky.social
Chenyan Jia
@jiachenyan.bsky.social
57 followers 53 following 1 posts
Assistant Professor @Northeastern. Previously @Stanford postdoc. @UTAustin @PKU1898 alum. Interested in human-centered AI, HCI, and misinformation.
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Reposted by Chenyan Jia
stanfordpacs.bsky.social
Hear more from co-author @jiachenyan.bsky.social:
New research reveals algorithms’ hidden political power
New research hijacks social media platform rankings to study how great an impact the algorithm has on political polarization.
bit.ly
December 12, 2025 at 5:34 PM
Reposted by Chenyan Jia
stanfordpacs.bsky.social
New research led by Faculty Affiliate @mbernst.bsky.social, former Postdoctoral Fellow @jiachenyan.bsky.social, and fellow scholars sheds light on the role of social media algorithms in driving political polarization and outlines new paths forward for both social media users and researchers.
Social media research tool lowers the political temperature
A new method created by Stanford researchers reduces polarization by downranking antidemocratic and highly partisan posts on X.
stanford.io
December 12, 2025 at 5:34 PM
Reposted by Chenyan Jia
dgrand.bsky.social
🚨 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
December 4, 2025 at 8:43 PM
Reposted by Chenyan Jia
ischool.uw.edu
What if you could see fewer hostile political posts on social media? A new paper out in Science by Martin Saveski @msaveski.bsky.social of the iSchool, along with @tiziano.bsky.social, @jiachenyan.bsky.social, Jeff Hancock, Jeanne Tsai and @mbernst.bsky.social, explores this: doi.org/10.1126/scie...
Screenshot of research article in Science titled "Reranking partisan animosity in algorithmic social media feeds alters affective polarization." Full text available at https://www.science.org/doi/10.1126/science.adu5584.
December 4, 2025 at 10:14 PM
Reposted by Chenyan Jia
mbernst.bsky.social
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.
mbernst.bsky.social Michael Bernstein @mbernst.bsky.social · 19d
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
December 3, 2025 at 7:46 PM
jiachenyan.bsky.social
Our new study in @science.org built an LLM-powered browser extension to rerank social media feeds without requiring platform cooperation. In a preregistered 10-day field experiment (N=1,256), we found that algorithmic ranking can both raise and lower levels of affective political polarization.
December 3, 2025 at 12:45 AM
Reposted by Chenyan Jia
caseynewton.bsky.social
A novel new study finds you can reduce polarization on X simply with a simple change to the algorithm. I spoke with the researchers: www.platformer.news/stanford-pol...
The results: reducing exposure to divisive content improved feelings toward the other party by about 2 points on a 100-point scale. On the other hand, boosting that content cooled feelings by about 2.5 points. The researchers say the shift is equivalent to about three years of rising partisan animosity in America, created (or reversed) in just a week. Remarkably, researchers found this result without adding or removing hyper-partisan material — it simply changed the order in which the posts appear. (For people who saw less polarizing content, for example, it was still in their feeds — just way lower, and thus less likely to be seen.) “I think the general intuition is that there's a little bit of a failure of imagination going on with a lot of the platforms,” Michael S. Bernstein, a researcher at Stanford and co-author of the paper, told me in an interview. “We think they were sort of painted into a box where they felt like they had to optimize for certain engagement criteria, like that's the only thing they could do. It just felt like that didn't necessarily have to be the case anymore."
December 2, 2025 at 12:58 AM
Reposted by Chenyan Jia
msaveski.bsky.social
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.
December 1, 2025 at 7:59 AM
Reposted by Chenyan Jia
msaveski.bsky.social
Joint work with @tiziano.bsky.social, @jiachenyan.bsky.social, Jeff Hancock, Jeanne Tsai, @mbernst.bsky.social

Link: doi.org/10.1126/scie...

And a very thoughtful perspective by @jennyallen.bsky.social and @jatucker.bsky.social: doi.org/10.1126/scie...

/1
December 1, 2025 at 7:59 AM
Reposted by Chenyan Jia
mbernst.bsky.social
Article with @tiziano.bsky.social, @msaveski.bsky.social, @jiachenyan.bsky.social, Jeff Hancock, and Jeanne Tsai

www.science.org/doi/10.1126/...
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...
www.science.org
December 1, 2025 at 7:33 PM
Reposted by Chenyan Jia
mbernst.bsky.social
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
December 1, 2025 at 7:33 PM
Reposted by Chenyan Jia
mbernst.bsky.social
Realtime LLM social media reranking enables field experiments demonstrating how we can depolarize. Congratulations @tiziano.bsky.social, @msaveski.bsky.social, and @jiachenyan.bsky.social!
tiziano.bsky.social Tiziano Piccardi @tiziano.bsky.social · Nov 25
New paper: Do social media algorithms shape affective polarization?

We ran a field experiment on X/Twitter (N=1,256) using LLMs to rerank content in real-time, adjusting exposure to polarizing posts. Result: Algorithmic ranking impacts feelings toward the political outgroup! 🧵⬇️
November 26, 2024 at 12:08 AM