TRSM Research Seminar by Yisheng Li

Title: Homophily in information systems – A review of computational methods

Abstract: Homophily is both a principle for social group formation with like-minded people as well as a mechanism for social interactions. Recent years have seen a growing body of management research on homophily particularly on large scale social media and digital platforms. However, the predominant traditional qualitative and quantitative methods employed face validity issues and/or are not well-suited for big social data. There are scant guidelines for applying computational methods to specific research domains concerning descriptive patterns, explanatory mechanisms, or predictive indicators of homophily. To fill this research gap, this paper offers a structured review of the emerging literature on computational social science approaches to homophily with a particular emphasis on their relevance, appropriateness, and importance to information systems research. We derive a research taxonomy for homophily and offer methodological reflections and recommendations to help inform future research.

Bio: Yisheng is a PhD in Management student at Toronto Metropolitan University. He is investigating the formation of homophily of conspiracy theories through the lens of social/cognitive theories. He aims to employ machine learning and natural language processing coupled with big data. Broadly, he is interested in the ethical implications of AI (e.g., fairness, transparency).