The lived experience of gambling-related harm in natural language.

Abstract

Gambling-related harms can have a significant negative impact on disordered gamblers, lower risk gamblers, and affected others. Yet, most disordered and lower risk gamblers will never seek formal treatment, often due to the stigma and shame surrounding gambling. Online self-help forums are a popular alternative way for gamblers to anonymously seek help from others. Analysis of these interactions can provide a deeper understanding of gambling than more commonly used research methodologies. In the present study, we leverage recent developments in natural language processing to analyze posts on a U.K.-based online self-help gambling forum. Using correlated topic modeling, we canvass the various types of discussions among forum members. We also combine this approach with semantic similarity analysis based on sentence embeddings, to map first the posts, and then the 10 topics, onto six previously established gambling-related harm domains. The topic modeling revealed a cluster of discussions of many negative emotions, a topic regarding the positive emotions underlying the potential for change, a distinct topic regarding gambling's relationship harms, and numerous environmental factors that contributed to harm. Emotional/psychological and health harms were most strongly associated with users' posts, illustrating the multidimensionality of severe gambling-related harm. Our results reveal the co-occurrence of different harms, such as the frequent mentions of financial harms and concomitant emotional/psychological harms. The analysis of the lived experiences of gambling-related harm in natural language represents a useful tool for gambling research and can provide a different perspective to inform policy. (PsycInfo Database Record (c) 2024 APA, all rights reserved).