CSSLab at Penn

What happens when scientific claims go beyond what a study can establish? In new Nature Human Behaviour research, Calvin, Timothy, Neil, Grace, and Duncan find causal claims in 46% of 194,631 cross-sectional studies and show that causal wording makes readers more likely to infer causation

The broader lesson is that improving science is not only about collecting better data. It is also about making sure the claims built on that data accurately reflect the evidence underneath them, and that those limits are preserved when research is communicated and summarized

What happens when scientific claims go beyond what a study can establish? In new Nature Human Behaviour research, Calvin, Timothy, Neil, Grace, and Duncan find causal claims in 46% of 194,631 cross-sectional studies and show that causal wording makes readers more likely to infer causation

In other words, how LLMs are instructed to summarize scientific evidence can affect whether those evidentiary limits are preserved.

What happens when scientific claims go beyond what a study can establish? In new Nature Human Behaviour research, Calvin, Timothy, Neil, Grace, and Duncan find causal claims in 46% of 194,631 cross-sectional studies and show that causal wording makes readers more likely to infer causation

That result points to a challenge for science communication. Prompts intended to make research easier to understand or more useful can inadvertently make its conclusions sound stronger than the evidence warrants

What happens when scientific claims go beyond what a study can establish? In new Nature Human Behaviour research, Calvin, Timothy, Neil, Grace, and Duncan find causal claims in 46% of 194,631 cross-sectional studies and show that causal wording makes readers more likely to infer causation

Using correlational wording alone did not prevent causal inference. Readers often still inferred causality. A brief note on the study design and what it could and could not establish reduced this over-inference, although the effect was small

What happens when scientific claims go beyond what a study can establish? In new Nature Human Behaviour research, Calvin, Timothy, Neil, Grace, and Duncan find causal claims in 46% of 194,631 cross-sectional studies and show that causal wording makes readers more likely to infer causation

How common is it? The researchers examined 194,631 cross-sectional studies, which generally provide evidence of association rather than causation. They found causal claims in about 46% of titles or abstracts, with causal overclaiming increasing roughly threefold from 2000 to 2024

What happens when scientific claims go beyond what a study can establish? In new Nature Human Behaviour research, Calvin, Timothy, Neil, Grace, and Duncan find causal claims in 46% of 194,631 cross-sectional studies and show that causal wording makes readers more likely to infer causation

Congratulations, Dr. Samar Haider! 🎓🖥️ @samarhaider.bsky.social Samar successfully defended his dissertation, "A Computational Approach to News Production", studying how computational methods can help us systematically understand what news organizations choose to cover and how they cover it.

At the CSS Lab, Samar has also been a leading member of our Media Bias Detector mediabiasdetector.seas.upenn.edu

Measures like "stops🛑" are building blocks for studying human mobility. But how reliably can we identify them in sparse GPS data?🛰️📍 At the CSS Showcase, Paco Barreras, Andrés Mondragón, and Caroline Chen shared work on the robustness of stop detection in sparse location data

The results show that different approaches can achieve similar accuracy when tuned to individual trajectories, but vary in robustness across a dataset. Smaller spatial thresholds can split or miss stops, while larger ones can merge neighboring stops

Measures like "stops🛑" are building blocks for studying human mobility. But how reliably can we identify them in sparse GPS data?🛰️📍 At the CSS Showcase, Paco Barreras, Andrés Mondragón, and Caroline Chen shared work on the robustness of stop detection in sparse location data

Using nearly 19,000 human survey responses collected across 40 waves during the 2024 U.S. presidential election, Elliot Pickens(@elliot-p.bsky.social), David Rothschild, Jenny Wang, and Jonas Mikhaeil compared LLM-generated and human survey responses. #IC2S2

Samar Haider @samarhaider.bsky.social presented Whose Voice Matters? Measuring Political Representation in News Quotations, with Chris Callison-Burch and Duncan Watts @duncanjwatts.bsky.social! #IC2S2

Amir Tohidi @atohidi.bsky.social presented at #IC2S2 Measuring "Copaganda": An Analysis of How U.S. National News Covers Crime, with Baird Howland, Billy Pierce, Duncan Watts @duncanjwatts.bsky.social, and David Rothschild @davmicrot.bsky.social.

At #AOM2026, Bethany Hsiao presented Fairness Preferences in Algorithmic Decision-Making as part of the symposium AI in Organizations: Challenges to Face and Opportunities to Embrace. @aomconnect.bsky.social

Where could you find the most CSS Lab folks in one place at #IC2S2? The poster session! Here are a few of the projects our lab members presented today.

Homa @homahm.bsky.social presented work with Pavel Savgira, Elisa Kreiss, and Amir Ghasemian @amir-ghasemian.bsky.social, on how LLM-generated summaries change the political lean of news articles by examining when summarization moderates political lean and when it preserves more partisan content.

Where could you find the most CSS Lab folks in one place at #IC2S2? The poster session! Here are a few of the projects our lab members presented today.

Timothy Dörr and Baird Howland presented their work on US and UK news media coverage of Gaza, leveraging GPT-5 to extract structured descriptions of violence. The study examines volume of coverage, emotional weight of violence descriptions, and naming of perpetrators.

Gayoung (@gayoung-jeon.bsky.social) led a hands-on tutorial on comprehensive TikTok data collection for computational social science at #IC2S2'26 Participants learned practical methods for collecting and analyzing TikTok data. Great work on a successful tutorial!