NYU's Center for Social Media, AI, and Politics

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.

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

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.

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.

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.

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.

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...

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.

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...

• 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.”

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...

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

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...

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)

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...

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.

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...

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.

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...

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.

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...

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.

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...

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