More Than a Bad Bet: Problem Gambling, Emotional Reactivity, and Athlete Cyberbullying Among Sports Bettors

Background/Purpose: The rapid expansion of legalized online sports betting in the United States has created new digital environments where fans interact directly with athletes, raising significant concerns about athlete cyberbullying (ACB). This is defined as the directed sending of negative messages, harmful images, or racist and sexist content to athletes via online platforms. While initial descriptive research has documented ACB prevalence among sports bettors, little empirical work has examined which individual-level factors predict engagement in these harmful behaviors. Understanding who is most likely to cyberbully athletes and the conditions that drive this behavior is essential for social workers seeking to develop targeted prevention strategies, inform platform policies, and advocate for athlete protections. This study addresses this gap by identifying which demographic, behavioral, and attitudinal factors predict ACB among online sports betters.

Methods: Cross-sectional survey data were collected from 1,013 adults who engage in sports betting. Respondents answered 45 items covering betting behaviors, online interactions with athletes, and demographics. The sample was predominantly White (70.5%), with a mean age of 42.2 years (SD = 12.8). Professional football (NFL) was the most popular sport to bet on (97.9%), and DraftKings was the most common platform (58.6%). Two binary outcome variables were constructed from seven self-reported ACB items: (1) any ACB engagement and (2) high-frequency ACB (several times a week or daily). Predictor variables included age, race (White vs. non-White), betting frequency, monthly wager amount, mood reactivity to betting losses, and self-reported problem gambling. Logistic regression models were estimated for each outcome.

Results: Over half (55.1%) of bettors reported engaging in at least one ACB behavior, and 34.5% reported high-frequency ACB. Four variables significantly predicted ACB across both models. Self-reported problem gambling was the strongest predictor, with bettors acknowledging adverse outcomes of gambling having approximately three times the odds of any ACB (OR = 2.96, p < .001) and high-frequency ACB (OR = 3.48, p < .001). Greater negative mood reactivity to losses predicted both any ACB (OR = 1.81, p < .001) and high-frequency ACB (OR = 1.71, p < .001). Higher monthly wager amounts predicted any ACB (OR = 1.46, p < .001) and high-frequency ACB (OR = 1.40, p = .001). Younger age was also significant across both models (OR = 0.97, p < .001). Notably, race and betting frequency were not significant predictors

Conclusions and Implications: Findings suggest that ACB is driven not by how often individuals bet, but by the financial stakes involved and the emotional and psychological vulnerability of the bettor. Problem gambling emerged as the most powerful predictor, indicating that ACB may function as one behavioral manifestation of broader gambling-related harm. These results have direct implications for social work practice: screening for problem gambling in digital wellness interventions, developing targeted psychoeducation for younger bettors who wager larger amounts, and advocating for platform accountability policies that identify and intervene with emotionally reactive users. This study offers a novel empirical contribution to social work literature at the intersection of digital technology, sports culture, and behavioral health.