Computing for social science: Characterizing, quantifying, and analyzing social phenomena in technology mediated communications

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Hutto, Clayton J.
Gilbert, Eric
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Traditional social science methods of analyzing unstructured and semi-structured qualitative content often rely on labor and time intensive methods to transform qualitative data into quantitative representations of phenomena of interest. In order to rapidly conduct such social scientific research on large-scale data, social science researchers need to incorporate computational tools and methods. The Computational Social Science (CSS) paradigm offers useful perspectives for gaining insights from large-scale analyses of demographic, behavioral, social network, and technology-mediated communication data to investigate human activity, relationships, and other phenomena at multiple scales (e.g., individual, organizational, community, social group, and societal). Human-Centered Computing (HCC) complements CSS in this context by offering foundational science for designing, developing, evaluating, and deploying computational artifacts that better support the human endeavors associated with the conduct and practice of CSS research. This dissertation demonstrates theoretical, methodological, and technological contributions resulting from blending traditional social science with computational approaches for the study of human cognition and behavior. Following the CSS paradigm, I build theoretically-informed representations of social constructs—e.g., models of interpersonal relationships and the complex cognitive processes related to human perceptions of sentiment and bias—using HCC methods and principles to develop and evaluate computational tools that implement those models for the purpose of aiding social science research oriented around large-scale text-based analysis of content from social media networks, product and movie reviews, and newspapers.
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