Our research on ideology and polarization explores how digital platforms amplify partisan divides and shape political discourse across societal contexts. It focuses on understanding the expression, measurement, and impact of ideological differences in shaping public opinion, both within and across linguistic and cultural boundaries. This area highlights the interplay between technology, political behavior, and societal cohesion in an increasingly interconnected world.
Selected Publications
Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada
The COVID-19 pandemic sparked rigorous debate about public health measures online, as the timing and extent of interventions unfolded. This project evaluates partisanship and the geographical distribution of public opinion on lockdowns, masks, and vaccines using the U.S. and Canada as case studies.
An Evaluation of Language Models for Hyperpartisan Ideology Detection in Persian Twitter
While LLMs have demonstrated how effective they can be for tasks in the English language, such as detecting social media users’ political ideology, their performance in other languages remains understudied. We contribute to this area of research by fine-tuning smaller LLMs to identify hyperpartisans in Persian social media, and compare the results to those from open-source and commercial models.
Party Prediction for Twitter
Partisanship has increasingly become a major point of contention in public discourse online, and as a result, researchers have developed a variety of methods to evaluate the party affiliations of users on social media. In this paper, we evaluate the performance of party prediction tools and propose new methods that are comparable or improve upon existing works.