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Jen Pan

@jenpans.bsky.social
70 followers 25 following 6 posts

Professor of Communication @Stanford

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Reposted by Jen Pan
Brendan Nyhan @brendannyhan.bsky.social · 07/05/2026
Our chapter! preprints.apsanet.org/engage/apsa/... "AI is more likely to reinforce existing patterns of [information] exposure and behavior than it is to transform how people understand and relate to the political world" (w/@jenpans.bsky.social @aasiegel.bsky.social @yamilrvelez.bsky.social)
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Jen Pan @jenpans.bsky.social · 08/04/2026
This was a huge team effort led with an amazing group of co-authors Ruth Appel @youngmiekim.bsky.social @yiqingxu.bsky.social @jatucker.bsky.social @taliastroud.bsky.social and many more. Grateful to everyone involved. Full paper: rdcu.be/fbU8b
rdcu.be
How deceptive online networks reached millions in the US 2020 elections
Nature Human Behaviour - Appel et al. found that deceptive networks reached over 37 million Facebook and 3 million Instagram users during the 2020 US elections, with the majority of this exposure...
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Jen Pan @jenpans.bsky.social · 08/04/2026
Deceptive networks disproportionately reached users who were older, more conservative, more frequently exposed to untrustworthy sources, and spent more time on Facebook.
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Jen Pan @jenpans.bsky.social · 08/04/2026
Regular users played a key role. Reshares by accounts unaffiliated with the networks were a major channel for spread—meaning countermeasures targeting only the networks themselves may not be enough.
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Jen Pan @jenpans.bsky.social · 08/04/2026
Reach was highly concentrated: just 3 of 49 networks drove over 70% of exposure, and 2 of those 3 were financially motivated, not politically motivated—yet they produced political content at scale.
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Jen Pan @jenpans.bsky.social · 08/04/2026
New study out in Nature Human Behaviour: 37 million US users were exposed to deceptive networks on Facebook & 3 million on Instagram during the 2020 elections—roughly 15% and 2% of active users. 🧵
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Jen Pan @jenpans.bsky.social · 11/03/2026
New paper with @xuxupolitics.bsky.social in @pnasnexus.org: Chinese LLMs refuse politically sensitive questions at far higher rates than US models — and it's not explained by training data or market differences. Government regulation appears to be a key driver. ow.ly/Y7UR50Yl8KA
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Reposted by Jen Pan
Xu Xu @xuxupolitics.bsky.social · 17/02/2026
China’s chatbots are censored by the state. In our @pnasnexus.org paper with @jenpans.bsky.social, we find substantially higher levels of political censorship in large language models (LLMs) originating from China than those developed outside China. doi.org/10.1093/pnas... 🧵
doi.org
Political censorship in large language models originating from China
Abstract. A growing body of research on large language models (LLMs) has identified various biases, primarily in contexts where biases reflect societal pat
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Reposted by Jen Pan
Xu Xu @xuxupolitics.bsky.social · 06/10/2025
Why do authoritarian states charge political opponents with non-political crimes? In our @thejop.bsky.social paper with Jennifer Pan & @yiqingxu.bsky.social, we examine how *Disguised Repression* undermines opponents’ moral authority and mobilization capacity. doi.org/10.1086/7342...
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