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While recent advances in natural language processing (NLP) have greatly enhanced our ability to analyze online text, distilling broad social-oriented research questions into tasks concrete enough for NLP models remains challenging. In this work, we develop state-of-the-art NLP models grounded in frameworks from social theory in order to analyze two social media movements: online media coverage of the #MeToo movement in 2017-2018 and tweets about #BlackLivesMatter protests in 2020.

In the first part, we show that despite common perception of the #MeToo movement as empowering, media coverage of events often portrayed women as sympathetic but unpowerful. In the second, we show that positive emotions like hope and optimism are prevalent in tweets with pro-BlackLivesMatter hashtags and significantly correlated with the presence of on-the-ground protests, whereas anger and disgust are not.  These results contrast stereotypical portrayals of protesters as perpetuating anger and outrage.  Overall, our work provides insight into social movements and debunks harmful stereotypes. We aim to bridge the gap between NLP, where models are often not designed to address social-oriented questions, and computational social science, where state-of-the-art NLP has often been underutilized.

Bio:

Anjalie Field is a Ph.D. candidate at the Language Technologies Institute at Carnegie Mellon University and a visiting student at the University of Washington, where she is advised by Yulia Tsvtekov. Her work focuses on the intersection of NLP and computational social science, including both developing NLP models that are socially aware and using NLP models to examine social issues like propaganda, stereotypes, and prejudice. She has presented her work in NLP and interdisciplinary conferences, receiving a nomination for best paper at SocInfo 2020, and she is also the recipient of a NSF graduate research fellowship and a Google PhD fellowship. Prior to graduate school, she received her undergraduate degree in computer science, with minors in Latin and ancient Greek, from Princeton University.

This seminar is co-sponsored by CITP and the Center for Statistics and Machine Learning.