Text Mining for Social Good; Context-aware Measurement of Social Impact and Effects Using Natural Language Processing

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 reading, and machine learning. The 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 aims to advance social good for individuals and society, and (2) the impact of social and individual biases on people's language use. This work contributes to advancing knowledge, theory and computational solutions relevant to the field of computational social science. The approaches and insights discussed can provide a better understanding of people's attitudes and judgments toward issues and events of general interest, which is necessary to develop solutions for minimizing biases, filter bubbles, and polarization while also improving the effectiveness of interpersonal and societal discourse.