Building Language Technologies for Social Good
We live in an era where many aspects of our daily activities are recorded as textual and activity data, from social media posts, to medical and financial records, to work activities captured by Wikipedia and other online tools. My research combines techniques in natural language processing, machine learning, and theories in social science to study human behavior in online communities, with the goal of developing theories and systems to build better socio-technical systems. In this talk, I will explain my research from two specific studies. The first one focuses on modeling how people seek and offer support via language in online cancer support communities, and the second studies what makes language persuasive by introducing a semi-supervised neural network to recognize persuasion strategies in loan requests on crowdfunding platforms. Through these two examples, I show how we can accurately and efficiently model human communication to build better social systems.
Diyi Yang is an assistant professor in the School of Interactive Computing at Georgia Tech, also affiliated with the Machine Learning Center (ML@GT) at Georgia Tech. Diyi received her Ph.D. from the Language Technologies Institute at Carnegie Mellon University, and her bachelor's degree from Shanghai Jiao Tong University, China. She is interested in Computational Social Science, and Natural Language Processing. She has published at leading NLP/HCI conferences and journals, and received one Notable Dataset Award from EMNLP 2015, one Best Paper Honorable Mention from ICWSM 2016, and two Best Paper Honorable Mentions from SIGCHI 2019. Diyi was awarded Carnegie Mellon Presidential Fellowship and Facebook Ph.D. Fellowship.