Doctoral candidate Rezvaneh (Shadi) Rezapour will present her research at the 2nd Annual Michigan Institute Consortium for Data Scientists in Training, a virtual event held from October 29-30. Rezapour is part of the Institute’s 2020 cohort, which includes researchers from 28 universities. The competitive program offers graduate students and postdocs the opportunity to participate in research talks, networking sessions, and mentoring opportunities.
Rezapour will give the talk, "Text Mining for Social Good; Context-aware Measurement of Social Impact and Effects Using Natural Language Processing."
Abstract: Exposure to information sources of different types and modalities, such as social media, movies, scholarly reports, and interactions with other communities and groups can change a person’s values as well as their knowledge and attitude towards various social phenomena. My doctoral research aims to analyze the effect of these stimuli on people and groups by applying mixed-method approaches that include techniques from natural language processing, close readings, and machine learning. This research leverages different types of user-generated texts (i.e., social media and customer reviews), and professionally-generated texts (i.e., scholarly publications and organizational documents) to study (1) the impact of information that was produced with the aim of advancing social good for individuals and society, and (2) the impact of social and individual biases and values on people's language use. This work contributes to advancing knowledge, theory, and computational solutions in the field of computational social science. The approaches and insights discussed can provide a better understanding of people's attitudes and judgment towards issues and events of general interest, which is necessary for developing solutions for minimizing biases, filter bubbles, and polarization while also improving the effectiveness of interpersonal and societal discourse.
Rezapour is conducting research on topics related to natural language processing, machine learning, and network analysis. She holds an MS in information management from Illinois.