IC Colloquium: Quantifying social organization and political polarization in online platforms
Event details
| Date | 09.12.2021 |
| Hour | 16:15 › 17:15 |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
By: Ashton Anderson - University of Toronto
Video of his talk
Abstract
Via mass selection into like-minded groups, online society may be becoming more fragmented and polarized, particularly with respect to partisan differences. However, our ability to measure the social makeup of online communities, and in turn understand the social organization 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 social dimensions by leveraging large-scale patterns of aggregate behaviour. Applying our methodology to 5.1B Reddit comments made in 10K communities over 14 years, we measure how the macroscale community structure is organized with respect to age, gender, and U.S. political partisanship. Examining political content, we find Reddit underwent a significant polarization event around the 2016 U.S. presidential election. Contrary to conventional wisdom, however, individual-level polarization is rare; the system-level shift in 2016 was disproportionately driven by the arrival of new users. Political polarization on Reddit is unrelated to previous activity on the platform, and is instead temporally aligned with external events.
We also observe a stark ideological asymmetry, with the sharp increase in 2016 being entirely attributable to changes in right-wing activity. Our methodology is broadly applicable to the study of online interaction, and our findings have implications for the design of online platforms, understanding the social contexts of online behaviour, and quantifying the dynamics and mechanisms of online polarization.
Bio
Ashton Anderson is an Assistant Professor of Computer Science at the University of Toronto, where he is also a Faculty Affiliate with 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 venues including the Proceedings of the National Academy of Sciences, the International Conference on Machine Learning, and the Web Conference.
More information
Video of his talk
Abstract
Via mass selection into like-minded groups, online society may be becoming more fragmented and polarized, particularly with respect to partisan differences. However, our ability to measure the social makeup of online communities, and in turn understand the social organization 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 social dimensions by leveraging large-scale patterns of aggregate behaviour. Applying our methodology to 5.1B Reddit comments made in 10K communities over 14 years, we measure how the macroscale community structure is organized with respect to age, gender, and U.S. political partisanship. Examining political content, we find Reddit underwent a significant polarization event around the 2016 U.S. presidential election. Contrary to conventional wisdom, however, individual-level polarization is rare; the system-level shift in 2016 was disproportionately driven by the arrival of new users. Political polarization on Reddit is unrelated to previous activity on the platform, and is instead temporally aligned with external events.
We also observe a stark ideological asymmetry, with the sharp increase in 2016 being entirely attributable to changes in right-wing activity. Our methodology is broadly applicable to the study of online interaction, and our findings have implications for the design of online platforms, understanding the social contexts of online behaviour, and quantifying the dynamics and mechanisms of online polarization.
Bio
Ashton Anderson is an Assistant Professor of Computer Science at the University of Toronto, where he is also a Faculty Affiliate with 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 venues including the Proceedings of the National Academy of Sciences, the International Conference on Machine Learning, and the Web Conference.
More information
Practical information
- General public
- Free
Contact
- Host: Bob West