Stigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube comments
Abstract Background 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 under...
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BMC
2025-04-01
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| Series: | Harm Reduction Journal |
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| Online Access: | https://doi.org/10.1186/s12954-025-01169-0 |
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| author | Johannes Singer |
| author_facet | Johannes Singer |
| author_sort | Johannes Singer |
| collection | DOAJ |
| description | Abstract Background 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. |
| format | Article |
| id | doaj-art-ac0bf0fa6b794e7cb1613b673af40869 |
| institution | OA Journals |
| issn | 1477-7517 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Harm Reduction Journal |
| spelling | doaj-art-ac0bf0fa6b794e7cb1613b673af408692025-08-20T02:17:53ZengBMCHarm Reduction Journal1477-75172025-04-0122111710.1186/s12954-025-01169-0Stigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube commentsJohannes Singer0Gambling Research Center, University of HohenheimAbstract Background 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.https://doi.org/10.1186/s12954-025-01169-0StigmaSelf-stigmaGamblingGambling disorderPersonal responsibilitySocial media |
| spellingShingle | Johannes Singer Stigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube comments Harm Reduction Journal Stigma Self-stigma Gambling Gambling disorder Personal responsibility Social media |
| title | Stigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube comments |
| title_full | Stigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube comments |
| title_fullStr | Stigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube comments |
| title_full_unstemmed | Stigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube comments |
| title_short | Stigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube comments |
| title_sort | stigmatisation of gambling disorder in social media a tailored deep learning approach for youtube comments |
| topic | Stigma Self-stigma Gambling Gambling disorder Personal responsibility Social media |
| url | https://doi.org/10.1186/s12954-025-01169-0 |
| work_keys_str_mv | AT johannessinger stigmatisationofgamblingdisorderinsocialmediaatailoreddeeplearningapproachforyoutubecomments |