DS/CSS Seminar: Ashton Anderson
12:00 pm -
The Cultural Structure of Online Platforms
Abstract: Our ability to measure the cultural makeup of online communities, and in turn understand the cultural structure of online platforms, is limited by the pseudonymous, unstructured, and large-scale nature of digital discussion. We develop a neural embedding methodology to quantify the positioning of online communities along cultural dimensions by leveraging large-scale patterns of aggregate behavior. Applying our methodology to 4.8B Reddit comments made in 10K communities over 14 years, we find that the macro-scale community structure is organized along cultural lines, and that relationships between online cultural concepts are more complex than simply reflecting their offline analogues. Examining political content, we show Reddit underwent a significant polarization event around the 2016 U.S. presidential election, and remained highly polarized for years afterward. Contrary to conventional wisdom, however, instances of individual users becoming more polarized over time are rare; the majority of platform-level polarization is driven by the arrival of new and newly political users. Our methodology is broadly applicable to the study of online culture, and our findings have implications for the design of online platforms, understanding the cultural contexts of online content, and quantifying cultural shifts in online behavior.
Speaker Bio: Ashton Anderson is an Assistant Professor of Computer Science at the University of Toronto, where he is also a Faculty Affiliate with both the Vector Institute and the Schwartz-Reisman Institute for Technology and Society. He received his PhD from Stanford University in 2015 and completed a postdoctoral appointment at Microsoft Research NYC in 2017. His research in computational social science encompasses a diverse range of questions at the intersection of AI, data, and society. His work has appeared in prestigious venues including the Proceedings of the National Academy of Sciences, Management Science, and the International Conference on Machine Learning. He received a best paper runner-up award at WWW 2014, was invited to the 2016 TKDD Special Issue of Best Papers of KDD 2016, and won the 2012–2015 Google PhD Fellowship in Social Computing.