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

Abstract

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.

Artificial intelligence development and dissemination impact on the sports industry labor market

“…These professionals are responsible for interpreting data, identifying patterns, and providing recommendations to enhance team performance, player scouting, and fan engagement. The demand for sports data analysts has increased significantly due to the utilization of advanced AI algorithms and machine learning techniques in data analysis ( 19 ).…”

“…Professionals should possess strong skills in data preprocessing, statistical analysis, and machine learning techniques. They should be able to apply algorithms and models to extract meaningful insights from sports data ( 19 ), enabling data-driven decision-making.…”

“…To this end, AI is rapidly transforming the sports industry, with a range of potential applications that can improve performance, judging, and optimize various aspects of sports marketing and management ( 1 , 19 ). As the technology continues to evolve, innovative applications of AI in sports are expected in the future ( 2 , 16 ).…”

“…However, there are also potential challenges and risks that need to be considered, such as the ethical implications of AI applications in sports, the potential for job displacement in certain sectors, and the need for appropriate regulations to ensure fair play. Overall, the development and dissemination of AI in the sports industry offers both opportunities and challenges that require careful consideration and management ( 1 , 19 ).…”

Stakeholders' “voice” through social media: the case of corporate social responsibility in sport

“…Social media's strong availability in the sport sphere has provided connectivity opportunities for members of the public to respond to the organization's activity. Considering the frequent interaction between public and sport organizations in social media space, it is natural for researchers to draw data from these platforms (Du et al ., 2023). For example, in other industries, a company's CSR tweet may not be disseminated quickly because of limited media exposure; as such, the likelihood of members of the public seeing the content is relatively low, not to mention the response.…”