The present study explored the topics and sentiment associated with gambling addiction during the COVID-19 pandemic, using topic modeling and sentiment analysis on tweets in English posted between 17-24th April 2020. The study was exploratory in nature, with its main objective consisting of inductively identifying topics embedded in user-generated content. We found that a five-topic model was the best in representing the data corpus, including: (i) the public's perception of gambling addiction amid the COVID-19 outbreak, (ii) risks and support available for those who stay at home, (iii) the users' interpretation of gambling addiction, (iv) forms of gambling during the pandemic, and (v) gambling advertising and impact on families. Sentiment analysis showed a prevalence of underlying fear, trust, sadness, and anger, across the corpus. Users viewed the pandemic as a driver of problematic gambling behaviors, possibly exposing unprepared individuals and communities to forms of online gambling, with potential long-term consequences and a significant impact on health systems. Despite the limitations of the study, we hypothesize that enhancing the presence of mental health operators and practitioners treating problem gambling on social media might positively impact public mental health and help prevent health services from being overwhelmed, in times when healthcare resources are limited.
Keywords: Gambling; Twitter; addiction; sentiment analysis; topic modeling.