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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 03/09/2026

Harris seemingly pursued a mobilization strategy, focusing on ideologically liberal podcasts rather than persuadable demographic groups.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 03/09/2026

We show that such targeting may have led to a small increase in his vote share of young men relative to young women.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 03/09/2026

We provide evidence consistent with Trump targeting potentially receptive young male voters based on the gender-lean of non-political podcasts in what we refer to as the gendered media space of the podcast ecosystem.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 03/09/2026

This data is combined with both audience consumption data, and survey data (Edison Podcast Metrics data and original YouGov data) that included self-reported podcast consumption and presidential vote intention.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 03/09/2026

We transcribed every 2024 episode of the 276 most popular podcasts in the U.S. We then used LLMs to label the content of these 36,659 episodes for: political content, ideology, and gender expression.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 03/09/2026

Did podcasts really swing young men to Trump in 2024? We assembled the data to find out, and we are presenting it at APSA this Saturday, Sept 5, 2 to 3:30pm. Long awaited working paper link also attached below! If you have seen this before, we have new data, come check it out.

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Christopher Barrie @cbarrie.bsky.social · 22/07/2026

I have a new paper out in @sociologicalsci.bsky.social w/ the briliant @aybukeatalay.bsky.social and @aliaelkattan.com We show that what we pay attention to online is a lot more politically diverse than what public engagement signals would suggest... sociologicalscience.com/articles-v13...

sociologicalscience.com

Information Diets Are More Diverse in Attention Than in Engagement

Article: Information Diets Are More Diverse in Attention Than in Engagement | Sociological Science | Posted July 21, 2026

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Hannah Waight @hwaight.bsky.social · 13/05/2026

I’m excited to share a new paper in Nature that shows how large language models launder the strategic rhetoric of authoritarian states. Paper here: www.nature.com/articles/s41.... A thread.

nature.com

State media control influences large language models - Nature

Government-controlled media influences the output of large language models via their training data, and models queried in the languages of countries with lower media freedom show a stronger ...

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Sol Messing @solmg.bsky.social · 13/05/2026

New in Nature: LLMs give "the party line" in the languages of authoritarian regimes. This works when they control the media, which feeds pretraining data. We show more state control over the media means less critical LLMs. 6 studies spanning 38 languages & 13 models. Details ↓

Scatter plot of 38 countries plus China showing the proportion of LLM responses favorable to the regime in the local language vs. World Press Freedom Index score. Lower press freedom predicts more regime-favorable responses; all production models pooled.

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NYU Law Democracy Project @democracyproject.bsky.social · 12/02/2026

Next up in our "100 ideas" series — New by @jatucker.bsky.social @csmapnyu.org — "Embracing Platform Transparency in a Digital World to Strengthen Democracy" Part of @nyulaw.bsky.social Democracy Project's "100 Ideas in 100 Days" Read the full piece here: democracyproject.org/posts/embrac...

democracyproject.org

Embracing Platform Transparency in a Digital World to Strengthen Democracy

A broad range of views on democracy to help break the stalemate caused by partisan conflict.

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Matt DeVerna @matthewdeverna.com · 24/02/2026

@csmapnyu.org is hiring two postdocs. Amazing group, highly recommend applying.

csmapnyu.org

Work With Us - NYU’s Center for Social Media, AI, and Politics

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Sol Messing @solmg.bsky.social · 03/03/2026

You can just research things. New from @jatucker.bsky.social & me at @brookings: Coding agents like Claude Code and Codex will likely accelerate research AND undermine institutional structures we built to support it.

Google Trends chart showing interest in commercial coding agents increasing dramatically in early 2026

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Sol Messing @solmg.bsky.social · 04/02/2026

Last week the story was that TikTok censored anti-Trump/ICE/Pretti videos after the U.S. ownership change. We investigated with a large set of US TikTok data and found some interesting results, short thread...

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 28/01/2026

CSMaP is now the Center for Social Media, AI, and Politics (CSMAP). Our research has expanded beyond social media to include digital media more broadly—especially generative AI and large language models and their role in politics and public life. Learn more at csmapnyu.org

csmapnyu.org

NYU's Center for Social Media and Politics

Strengthening democracy by conducting rigorous research, advancing evidence-based public policy, and training the next generation of scholars.

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Melina Much @melinamuch.bsky.social · 17/12/2025

Is Joe Rogan really just a voice of the right? Our new @csmapnyu.org piece for @goodauth.bsky.social shows he’s just as much a space for the left and the center, too. A look inside today’s surprisingly complicated podcast information ecosystem. 🎙️ goodauthority.org/news/podcast...

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 01/12/2025

The results reveal both the promise and limits of AI labeling. Labels communicate provenance when correctly applied, but do not reliably shift belief, change engagement, or reduce misinformation risk. Suggesting that labeling alone is unlikely to counter the influence of synthetic political visuals.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 01/12/2025

🔎 The team finds evidence of a mixed pattern: exposure to labeled synthetic images can make some participants view unlabeled synthetic ones as more likely to be human-made — but this is offset by the broader skepticism about images being made by humans that label exposure also triggers.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 01/12/2025

• Belief and engagement remained unchanged. Labels did not reduce belief that the depicted event occurred, nor did they affect intentions to like, share, comment, or seek more information. 📌 A follow-up experiment tested whether labeled synthetic images create an “implied authenticity effect.”

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 01/12/2025

👉 Key findings: • AI labels can improve transparency when properly applied. Participants reliably inferred that labeled images were more likely created with AI, even when that wasn’t the case. +

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 01/12/2025

This enabled comparisons across both true and false political visuals. 🔍

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 01/12/2025

To build realistic stimuli, the team created synthetic images using ChatGPT-written prompts and Midjourney outputs, and paired them with visually similar real photos. They also found synthetic images of events that never happened, and matched them with authentic images from comparable contexts.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 01/12/2025

Across two online experiments, participants viewed both authentic and AI-generated political images — some labeled “Made with AI,” others unlabeled — and rated: • who created the image (provenance) • whether the event happened (veracity) • how likely they’d be to like, share, or comment (engagement)

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 01/12/2025

As generative AI becomes more accessible, synthetic political images are reshaping how people see and interpret events. One question remains: Do AI labels help the public navigate this environment? Our new preprint, It Works When It Works, tests exactly that. 🔗 osf.io/preprints/so...

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 14/11/2025

9/ Congratulations to the authors: Aaron Erlich, Kevin Aslett, Sarah Graham, and Joshua Tucker! @aaronerlich.bsky.social, @selisegraham.bsky.social ham.bsky.social, @jatucker.bsky.social @kevinaslett.bsky.social

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 14/11/2025

Taken together, the findings highlight that language itself can shape how people judge credibility in multilingual environments. Yet these effects are not uniform: they depend on which language a person prefers, and they don’t necessarily strengthen resilience against misinformation.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 14/11/2025

We also tested a popular media literacy intervention — “tips to spot false news” — that has been used by platforms like Facebook. While the intervention reduced belief in stories overall, it lowered belief in both true and false stories equally, producing no net gain in discernment. 👇👇

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 14/11/2025

But there was also a tradeoff. Reading in a less-preferred language reduced belief in true stories as well as false ones. In other words, language shifted credibility judgments, but it did not improve people’s ability to distinguish fact from misinformation.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 14/11/2025

The results were striking. Ukrainian-preferring respondents were less likely to believe both true and false stories when written in Russian. By contrast, Russian-preferring respondents sometimes showed greater belief in false stories when those same stories appeared in Ukrainian. 👇👇

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 14/11/2025

Our goal was simple yet important: to test whether individuals are more or less susceptible to believing false news stories when they are presented in people’s non-preferred language — and to determine if language itself functions as a credibility cue.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 14/11/2025

Participants were randomly assigned to read stories in their preferred language or their less-preferred language, within days of publication. 👇👇

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 14/11/2025

To study this, we asked bilingual Ukrainians to evaluate news articles in Ukrainian and Russian as to whether they were true, false or misleading, or couldn’t tell.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 14/11/2025

This means people encounter true and false information in two linguistic environments, one of which is also used in active disinformation campaigns and is the language of the invader in the current war.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 14/11/2025

Ukraine is a crucial case: most citizens are bilingual in Ukrainian and Russian, regularly consuming news in both languages.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 14/11/2025

When we think about false news stories, we usually focus on what is being said. But what if the language of a story shapes whether people believe it? Our new paper in the Journal of Experimental Political Science explores this question in Ukraine. www.cambridge.org/core/journal...

cambridge.org

How Language Shapes Belief in Misinformation: A Study Among Multilinguals in Ukraine | Journal of Experimental Political Science | Cambridge Core

How Language Shapes Belief in Misinformation: A Study Among Multilinguals in Ukraine

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Petter Törnberg @pettertornberg.com · 07/11/2025

LLMs are now widely used in social science as stand-ins for humans—assuming they can produce realistic, human-like text But... can they? We don’t actually know. In our new study, we develop a Computational Turing Test. And our findings are striking: LLMs may be far less human-like than we think.🧵

arxiv.org

Computational Turing Test Reveals Systematic Differences Between Human and AI Language

Large language models (LLMs) are increasingly used in the social sciences to simulate human behavior, based on the assumption that they can generate realistic, human-like text. Yet this assumption rem...

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Tiago Ventura @tiagoventura.bsky.social · 07/10/2025

The paper is co-authored with Bernhard Von Clemm, @ericka.bric.digital @jonathannagler.bsky.social @magdalenawojciesza.bsky.social This is also one of the projects I started at @csmapnyu.org ---- thanks to the entire lab involved! The paper can be found here: www.cambridge.org/core/journal...

cambridge.org

Survey Professionalism: New Evidence from Web Browsing Data | Political Analysis | Cambridge Core

Survey Professionalism: New Evidence from Web Browsing Data

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Tiago Ventura @tiagoventura.bsky.social · 07/10/2025

How common are “survey professionals” - people who take dozens of online surveys for pay - across online panels, and do they harm data quality? Our paper, FirstView at @politicalanalysis.bsky.social, tackles this question using browsing data from three U.S. samples (Facebook, YouGov, and Lucid):

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Sarah Shugars @shugars.bsky.social · 13/08/2025

Fascinating work from @jatucker.bsky.social and @csmapnyu.org looking at the effect of labeling images as AI on people’s beliefs about the provenance + veracity of that image. They also explore the implications of AI images not being labeled after seeing labeled images #PaCSS2025 #polnet2025

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Sarah Shugars @shugars.bsky.social · 13/08/2025

Exciting methodological development from Ben Guinadeau and @csmapnyu.org using poisson factorization to do ideal point estimation of TikTok posts #pacss2025 #polnet2025

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Sarah Shugars @shugars.bsky.social · 14/08/2025

Very interesting work from @hwaight.bsky.social and @csmapnyu.org examining narrative similarity between news stories from global media sources. Methodological challenging because stories may communicate the same ideas/claims without using the same ngrams #pacss2025 #polnet2025

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Melina Much @melinamuch.bsky.social · 14/08/2025

My awesome co-author with our poster about the GenZ gender gap and podcast consumption at PolNet/PACSS! If you didn’t see us here catch us at APSA at the PolCom preconference and Saturday first thing in the morning 💃🏻 @csmapnyu.org

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Tiago Ventura @tiagoventura.bsky.social · 17/07/2025

🚨 Paper now as "just accepted" at @The_JOP. We ran the first WhatsApp deactivation experiment focused on multimedia content ahead of the 2022 election in Brazil. We find a reduction in users' recall of false rumors -- and, to a smaller degree, of true news. Null effects on attitudes. Full thread ⬇️

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Christopher Barrie @cbarrie.bsky.social · 22/07/2025

In addition to the original UK results, we have now ***replicated*** this (TWICE) in the US. The main findings hold strong: information diets are a lot more diverse in attention than in engagement. New version here: osf.io/preprints/os...

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 17/07/2025

Congrats to the authors @tiagoventura.bsky.social, @rmajumdar.bsky.social, Jonathan Nagler, and @jatucker.bsky.social. The paper, which is accepted for publication at @The_JOP, can be found here: www.journals.uchicago.edu/doi/epdf/10....

journals.uchicago.edu

University of Chicago Press Journals: Cookie absent

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 17/07/2025

Our study was a rare field experiment on misinformation in the Global South, adding to a growing call to broaden the geographic and platform scope of causally identified misinformation research.

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NYU's Center for Social Media, AI, and Politics @csmapnyu.org · 17/07/2025

Big takeaway: WhatsApp matters—but changing exposure does not mechanically change attitudes in the short run. Political beliefs are hard to change and probably require long-term interventions.

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