Research Scientist in the Research and Development division at Educational Testing Service (ETS)
Algorithmic Fairness and Bias in Automated Test Scoring
The SIAI seminar series will feature speakers from academia, research labs, government agencies and the industry in topics of interest to the SIAI community.
With an increasing amount of data available online, we are now able to examine political information and behaviors through a new lens. In this talk, I will cover a series of studies that underline this promise for the study of news producers, citizens, and social movement organizations. First, focusing on the news media, I will characterize the spread of fake news during the 2016 Presidential elections. Through the use of heterogenous data, I will examine the interplay between news media production and consumption, social media behavior, and the information the electorate retained about the presidential candidates leading up to the election. Second, turning to the citizens, I will examine how individuals conform to community norms in political discussions. Past research identifies many processes that contribute to maintaining stable norms, including self-selection, pre-entry learning, post-entry learning, and retention. What is the relative importance of these processes? I will answer this question through an analysis of political subreddits on Reddit with stable and distinctive toxicity levels. Finally, by building predictive models to detect social movement organizations (SMOs) at scale, I will examine SMO participation in online social and political movements.