Machine Learning in Sport Social Media Research: Practical Uses and Opportunities

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James Du Department of Sport Management, Florida State University, Tallahassee, FL, USA

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Yoseph Z. Mamo Department of Human Movement Sciences, Old Dominion University, Norfolk, VA, USA

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Carter Floyd Department of Sport Management, Florida State University, Tallahassee, FL, USA

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Niveditha Karthikeyan Department of Sport Management, Florida State University, Tallahassee, FL, USA

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Jeffrey D. James Department of Sport Management, Florida State University, Tallahassee, FL, USA

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In tandem with the burgeoning popularity of social media research in the field of sport communication and marketing, we are witnessing a concomitant rise in its epistemological sophistication. Despite this growth, the field has given less attention to methodological issues and implications. In light of the development of machine learning, the overarching goal of the current research was to answer the call for innovative methodological approaches to advance knowledge in the area of social media research. Specifically, we (a) assess the current state of sport social media research from a methodological perspective, with a particular focus on machine learning; (b) present an empirical illustration to demonstrate how sport scholars can benefit from the advancement in natural language processing and the derivative topic modeling techniques; (c) discuss how machine learning could enhance the rigor of social media research and improve theory development; and (d) offer potential opportunities and directions for the future sport social media research that utilizes machine learning.

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