6 papers
Epistemic diversity across language models mitigates knowledge collapse
Damian Hodel, Jevin D. West
Artificial intelligence (AI) increasingly generates the very content used to train future AI systems. This feedback loop can degrade model quality, reduce informational diversity,…
Deep Graph Learning will stall without Network Science
Christopher Blöcker, Martin Rosvall, Ingo Scholtes +1
Deep graph learning focuses on flexible and generalizable models that learn patterns in an automated fashion. Network science focuses on models and measures revealing the organizat…
RIP Twitter API: A eulogy to its vast research contributions
Ryan Murtfeldt, Sejin Paik, Naomi Alterman +2
Since 2006, Twitter's APIs have been rich sources of data for researchers studying social phenomena such as misinformation, public communication, crisis response, and political beh…
The Role of Follow Networks and Twitter's Content Recommender on Partisan Skew and Rumor Exposure during the 2022 U.S. Midterm Election
Kayla Duskin, Joseph S. Schafer, Alexandros Efstratiou +2
Social media platforms shape users' experiences through the algorithmic systems they deploy. In this study, we examine to what extent Twitter's content recommender, in conjunction…
Are Widely Known Findings Easier to Retract?
Shahan Ali Memon, Jevin D. West, Cailin O'Connor
Failures of retraction are common in science. Why do these failures occur? And, relatedly, what makes findings harder or easier to retract? We use data from Microsoft Academic Grap…
From job titles to jawlines: Using context voids to study generative AI systems
Shahan Ali Memon, Soham De, Sungha Kang +5
In this paper, we introduce a speculative design methodology for studying the behavior of generative AI systems, framing design as a mode of inquiry. We propose bridging seemingly…