The COVID-19 pandemic is having an unprecedented impact on the leisure industry. Mandatory directives such as social distancing and stay-at-home /shelter-in-place orders reduce disease transmission and protect the health and well-being of the public. However, such strategies might impair active leisure participation. We identify challenges and constraints of engaging in active leisure activities during the pandemic and explore how the general public can use technology and big data analytics to negotiate constraints during this uncertain time. Creative applications of big data analytics demonstrate that negotiating active leisure constraints and battling the pandemic are not contradictory goals. We recommend society to harness the power of these data-driven tools to effectively navigate interpersonal, structural, and intrapersonal constraints to active leisure while improving the efficiency with which we combat the spread of COVID-19.
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.
Student-athletes at the Division I institutions face a slew of challenges and stressors that can have negative impacts in eliciting different emotional responses during the COVID-19 pandemic. We employed machine-learning-based natural language processing techniques to analyze the user-generated content posted on Twitter of Atlantic Coast Conference (ACC) student-athletes to study changes in their sentiment as it relates to the COVID-19 crisis, major societal events, and policy decisions. Our analysis found that positive sentiment slightly outweighed negative sentiment overall, but that there was a noticeable uptick in negative sentiment in May and June 2020 in conjunction with the Black Lives Matter protests. The most commonly expressed emotions by these athletes were joy, trust, anticipation, and fear, suggesting that they used social media as an outlet to share primarily optimistic sentiments, while still publicly expressing strong negative sentiments like fear and trepidation about the pandemic and other important contemporary events. Athletic administrators, ACC coaches, support staff, and other professionals can use findings like these to guide sound, evidence-based decision-making and to better track and promote the emotional wellness of student-athletes.
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