10 papers
Information Discernment in Large Language Models
Joshua Ashkinaze, Laura Kurek, Alina Faisal +4
LLMs are increasingly used with external knowledge sources like the internet. Do they weigh information appropriately -- updating more for reliable sources (source discernment) and…
Seeing Like an AI: How LLMs Apply (and Misapply) Wikipedia Neutrality Norms
Joshua Ashkinaze, Ruijia Guan, Laura Kurek +3
Large language models (LLMs) are trained on broad corpora and then used in communities with specialized norms. Is providing LLMs with community rules enough for models to follow th…
Framing Unionization on Facebook: Communication around Representation Elections in the United States
Arianna Pera, Veronica Jude, Ceren Budak +1
Digital media have become central to how labor unions communicate, organize, and sustain collective action. Yet little is known about how unions' online discourse relates to concre…
The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement
Jonathan Stray, Ian Baker, George Beknazar-Yuzbashev +42
We report the first direct comparisons of multiple alternative social media algorithms on multiple platforms on outcomes of societal interest. We used a browser extension to modify…
Deep Value Benchmark: Measuring Whether Models Generalize Deep Values or Shallow Preferences
Joshua Ashkinaze, Hua Shen, Saipranav Avula +2
We introduce the Deep Value Benchmark (DVB), an evaluation framework that directly tests whether large language models (LLMs) learn fundamental human values or merely surface-level…
Follow Nudges without Budges: A Field Experiment on Misinformation Followers Didn't Change Follow Networks
Laura Kurek, Joshua Ashkinaze, Ceren Budak +1
Can digital ads encourage users exposed to inaccurate information sources to follow accurate ones? We conduct a large-scale field experiment (N=28,582) on X, formerly Twitter, with…