CSSLab at Penn

Computational Social Science Lab at the University of Pennsylvania

[CSSLab at Penn @csspenn]

👩‍💻🧑‍💻👨‍💻 Want to apply LLMs to text analysis in your research?

Join Timothy Dörr (@Timothy95Doerr) and Baird Howland tomorrow for a hands-on workshop! 💻

They’ll share their work on how news media describe violence in international conflict, then guide participants through using LLMs for content analysis and text extraction.

No prior computational experience needed. Food will be served!

📅 Sept. 9, 2–5 PM
📍 ASC 300 @AnnenbergPenn
💻 Bring a laptop and Google account! No installation needed!

[CSSLab at Penn @csspenn]

📖 Read the open-access paper to explore the full findings https://t.co/fVwIicKxIR

[CSSLab at Penn @csspenn]

What happens when scientific claims go beyond what a study can establish?

In new research published in Nature Human Behaviour, Calvin Isch, Timothy Dörr (@Timothy95Doerr), Neil Fasching, Grace Jennings, and Duncan Watts (@duncanjwatts) find causal claims in 46% of 194,631 cross-sectional studies and show that causal wording makes readers more likely to infer causation.

They also find that asking LLMs to simplify research or discuss its practical implications can increase causal overclaiming 🧵⬇️

https://t.co/LusNAx3jhD

[CSSLab at Penn @csspenn]

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

[CSSLab at Penn @csspenn]

Beyond his own research, he has been an important part of the intellectual life of the lab. His thoughtful feedback, questions, and engagement have helped many of our projects move forward.

Congratulations again, Samar! We’re excited to see where you take this work next 🚀

[CSSLab at Penn @csspenn]

Congratulations, Dr. Samar Haider! 🎓🖥️
@samarhdr

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.

[CSSLab at Penn @csspenn]

At the CSS Lab, Samar has also been a leading member of our Media Bias Detector https://t.co/o6039g6MCf

[CSSLab at Penn @csspenn]

This work is part of the broader NOMAD effort to expand access to human mobility data and develop methods, tools, and guidance for analyzing it reliably!!

🛰️Learn more about NOMAD https://t.co/NzUI8RPpjT

[CSSLab at Penn @csspenn]

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

[CSSLab at Penn @csspenn]

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

[CSSLab at Penn @csspenn]

What have we been working on at the CSS Lab? 👀

A quick look inside our Spring Showcase, with lightning talks, posters, new tools, and works in progress across computational social science.

A few highlights from the day 📷 And of course, more to come!