Stigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube comments

dc.contributor.authorSinger, Johannes
dc.contributor.corporateSinger, Johannes; Gambling Research Center, University of Hohenheim, Schwerzstraße 44, 70599, Stuttgart, Germany
dc.date.accessioned2025-07-03T13:15:47Z
dc.date.available2025-07-03T13:15:47Z
dc.date.issued2025
dc.date.updated2025-07-03T13:03:53Z
dc.description.abstractBackground: The stigmatisation of gamblers, particularly those with a gambling disorder, and self-stigmatisation are considered substantial barriers to seeking help and treatment. To develop effective strategies to reduce the stigma associated with gambling disorder, it is essential to understand the prevailing stereotypes. This study examines the stigma surrounding gambling disorder in Germany, with a particular focus on user comments on the video platform YouTube. Methods: The study employed a deep learning approach, combining guided topic modelling and qualitative summative content analysis, to analyse comments on YouTube videos. Initially, 84,024 comments were collected from 34 videos. After review, two videos featuring a person who had overcome gambling addiction were selected. These videos received significant user engagement in the comment section. An extended stigma dictionary was created based on existing literature and embeddings from the collected data. Results: The results of the study indicate that there is substantial amount of stigmatisation of gambling disorder in the selected comments. Gamblers suffering from gambling disorder are blamed for their distress and accused of irresponsibility. Gambling disorder is seen as a consequence of moral failure. In addition to stigmatising statements, the comments suggest the interpretation that many users are unaware that addiction develops over a period of time and may require professional treatment. In particular, adolescents and young adults, a group with a high prevalence of gambling-related disorders and active engagement with social media, represent a key target for destigmatisation efforts. Conclusions: It is essential to address the stigmatisation of gambling disorder, particularly among younger populations, in order to develop effective strategies to support treatment and help-seeking. The use of social media offers a comprehensive platform for the dissemination of information and the reduction of the stigmatisation of gambling disorder, for example by strengthening certain models of addiction.en
dc.description.sponsorshipOpen Access funding enabled and organized by Projekt DEAL.
dc.description.sponsorshipUniversität Hohenheim (3153)
dc.identifier.urihttps://doi.org/10.1186/s12954-025-01169-0
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/17917
dc.language.isoeng
dc.rights.licensecc_by
dc.subjectStigma
dc.subjectSelf-stigma
dc.subjectGambling
dc.subjectGambling disorder
dc.subjectPersonal responsibility
dc.subjectSocial media
dc.subjectYouTube
dc.subjectGuided topic modelling
dc.subject.ddc360
dc.titleStigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube commentsen
dc.type.diniArticle
dcterms.bibliographicCitationHarm reduction journal, 22 (2025), 56. https://doi.org/10.1186/s12954-025-01169-0. ISSN: 1477-7517 London : BioMed Central
dcterms.bibliographicCitation.articlenumber56
dcterms.bibliographicCitation.issn1477-7517
dcterms.bibliographicCitation.journaltitleHarm reduction journal
dcterms.bibliographicCitation.originalpublishernameBioMed Central
dcterms.bibliographicCitation.originalpublisherplaceLondon
dcterms.bibliographicCitation.volume22
local.export.bibtex@article{Singer2025, doi = {10.1186/s12954-025-01169-0}, author = {Singer, Johannes}, title = {Stigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube comments}, journal = {Harm reduction journal}, year = {2025}, volume = {22}, }
local.export.bibtexAuthorSinger, Johannes
local.export.bibtexKeySinger2025
local.export.bibtexType@article
local.title.fullStigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube comments

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