Digital Traces and Social Ties: How Computational Social Science is Transforming Communication Research

Wednesday, 5 October 2022, 10:30am (HKT) on Zoom

This talk examines the growing importance of computational social science in communication research, arguing that the turn toward data science is transforming this work through increased attention to digital trace data, electronic text and digital visual content, and network connections and communication interactions, often in combination. This is certainly true (a) for work connecting communication and health management, especially in chronic care of individuals in recovery from cancer and addiction and (b) for research examining political contention in response to societal events as reflected in our polarized and asymmetric media ecologies. The frameworks for research shared in this talk emphasize the role of network mapping and the power of text analytics for addressing pressing communication and societal questions, with the dual U.S. crises of the opioid epidemic and mass shootings used as exemplars. Data collected through multiple NIH-funded studies of health technology and foundation funded projects tracking political news and social media also point to larger conclusions about the intersection of data science and communication research.

DHAVAN SHAH is a Maier-Bascom Professor at the University of Wisconsin-Madison, where he is Director of the Mass Communication Research Center (MCRC), Research Director of the Center for Communication and Civic Renewal (CCCR), and Scientific Director in the Center for Health Enhancement System Studies (CHESS). An abiding interest in the intersecting power of framing and social capital has shaped his research on: (1) the influence of message construction and processing, (2) the communication dynamics shaping civic participation, and (3) the effects of computer-mediated interactions on chronic disease management. This work has generated grants totaling nearly $55 million from private foundations and federal governments. He often applies computational approaches to social science questions, using natural language processing, network analytics, machine learning, and multi-modal classification to study communication in politics and health. His home is in the School of Journalism and Mass Communication, with affiliations in Industrial and Systems Engineering, Marketing, and Political Science.

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Organised by Research Postgraduate Studies Program, School of Communication