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

被引:7
|
作者
Du, James [1 ]
Mamo, Yoseph Z. [2 ]
Floyd, Carter [1 ]
Karthikeyan, Niveditha [1 ]
James, Jeffrey D. [1 ]
机构
[1] Florida State Univ, Dept Sport Management, Tallahassee, FL 32306 USA
[2] Old Dominion Univ, Dept Human Movement Sci, Norfolk, VA USA
关键词
BERTopic; natural language processing; sport marketing; BIG DATA; MANAGEMENT; EXPERIENCE; ENGAGEMENT; ANALYTICS;
D O I
10.1123/ijsc.2023-0151
中图分类号
G2 [信息与知识传播];
学科分类号
05 ; 0503 ;
摘要
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.
引用
收藏
页码:97 / 106
页数:10
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