SavedAdd your journal

Journals / Yeni Medya / 2026 / Sayı: 20

Text Mining Online Gambling Discourse in YouTube Comments: Thematic Structures, Emotional Patterns, and Narrative Dynamics

YouTube Yorumlarında Çevrim İçi Kumar Söyleminin Metin Madenciliği: Tematik Yapılar, Duygusal Yönelimler ve Anlatı Dinamikleri

Mahmut Bingöl(ZONGULDAK BÜLENT ECEVİT ÜNİVERSİTESİ)0000-0002-7334-0992
Mutluhan Çamur(ZONGULDAK BÜLENT ECEVİT ÜNİVERSİTESİ)0000-0002-3832-4911
Journal
Yeni Medya
Pages
39–66
Publication Date
2026-06-27
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Consequently, the study demonstrates that online gambling is established on digital platforms not merely as an individual behavior but as a multi-layered discursive space where emotional, moral, and structural negotiations of meaning take place.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines how online gambling is interpreted in YouTube user comments through specific thematic structures, emotional orientations, and discursive frames.
📊
Key Findings & Data:The research is based on user comments collected from videos posted in Türkiye after 2020 via the YouTube Data API.
💡
Conclusion & Impact:Consequently, the study demonstrates that online gambling is established on digital platforms not merely as an individual behavior but as a multi-layered discursive space where emotional, moral, and structural negotiations of meaning take place.
🔬Design:Modeling & Simulation
📍Scope:2020 Verileri
⚙️Variables:Çevrimiçi Kumar · BERTopic · Duygu Analizi

Abstract

This study examines how online gambling is interpreted in YouTube user comments through specific thematic structures, emotional orientations, and discursive frames. The research is based on user comments collected from videos posted in Türkiye after 2020 via the YouTube Data API. Following a multi-stage data collection and filtering process, the final analytical corpus was constructed from 22,707 comments derived from 131 videos. The study utilized the Turkish context-sensitive transformer-based BERTurk model for sentiment analysis and the BERTopic method for identifying thematic patterns. Model outputs were interpreted using a human-in-the-loop approach, and micro-topics were grouped under four main meta-themes: Destructive Narratives, Moral and Recovery Framing, Structural and Systemic Critique, and Gambling Practices. The analysis reveals that online gambling discourse is predominantly structured around narratives of loss, debt, addiction, and collapse, accompanied by moral recovery discourses and systemic critiques. Emotional distribution varies across the meta-themes, with negative emotions particularly concentrated in narratives of destruction and structural critiques. Additionally, comments classified as outliers in the standard topic modeling process were treated as a separate analytical layer, and through re-modeling, it was demonstrated that a significant portion of these comments are related to the existing thematic structures. This finding indicates that the discourse on online gambling consists not only of regular thematic patterns but also of fragmented and transitional narrative structures. Consequently, the study demonstrates that online gambling is established on digital platforms not merely as an individual behavior but as a multi-layered discursive space where emotional, moral, and structural negotiations of meaning take place.

Özet

Bu çalışma, çevrim içi kumarın YouTube kullanıcı yorumlarında hangi tematik yapılar, duygusal yönelimler ve söylemsel çerçeveler üzerinden anlamlandırıldığını incelemektedir. Araştırma, YouTube Data API aracılığıyla Türkiye bağlamında 2020 sonrası döneme ait videolardan derlenen kullanıcı yorumlarına dayanmaktadır. Çok aşamalı veri toplama ve filtreleme sürecinin ardından nihai analitik korpus 131 videodan elde edilen 22.707 yorumdan oluşturulmuştur. Çalışmada duygu analizi için Türkçe bağlama duyarlı transformer tabanlı BERTurk modeli, tematik örüntülerin belirlenmesi için ise BERTopic yöntemi kullanılmıştır. Model çıktıları, human-in-the-loop yaklaşımıyla yorumlanmış ve mikro-topicler dört ana meta-tema altında birleştirilmiştir: Yıkım Anlatıları (Destructive Narratives), Ahlaki ve İyileşme Çerçevesi (Moral and Recovery Framing), Yapısal ve Sistemik Eleştiri (Structural and Systemic Critique) ve Kumar Uygulamaları (Gambling Practices). Analiz, çevrim içi kumar söyleminin baskın olarak kayıp, borç, bağımlılık ve çöküş anlatıları etrafında kurulduğunu; buna ahlaki iyileşme söylemleri ve sistem eleştirilerinin eşlik ettiğini göstermektedir. Duygu dağılımı meta-temalar arasında farklılaşmakta, özellikle yıkım anlatıları ve yapısal eleştirilerde olumsuz duygu yoğunlaşmaktadır. Buna ek olarak, standart topic modeling sürecinde outlier olarak sınıflandırılan yorumlar ayrı bir analiz katmanı olarak ele alınmış ve yeniden modelleme yoluyla bu yorumların önemli bir kısmının mevcut tematik yapılarla ilişkili olduğu gösterilmiştir. Bu bulgu, çevrim içi kumar söyleminin yalnızca düzenli tematik örüntülerden değil, aynı zamanda parçalı ve geçişli anlatı yapılarından oluştuğunu ortaya koymaktadır. Sonuç olarak çalışma, çevrim içi kumarın dijital platformlarda yalnızca bireysel bir davranış değil, aynı zamanda duygusal, ahlaki ve yapısal anlam müzakerelerinin gerçekleştiği çok katmanlı bir söylem alanı olarak kurulduğunu göstermektedir.

🔖

Cite This Article

Bingöl, M., Çamur, M. (2026). Text Mining Online Gambling Discourse in YouTube Comments: Thematic Structures, Emotional Patterns, and Narrative Dynamics. Yeni Medya, Sayı: 20, 39–66. https://doi.org/10.55609/yenimedya.1869463

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (41)

  1. Aranda, A. M., Sele, K., Etchanchu, H., Guyt, J. Y., & Vaara, E. (2021). From big data to rich theory: Integrating critical discourse analysis with structural topic modeling. European Management Review, 18(3), 197–214. https://doi.org/10.1111/emre.12452
  2. Bernhard-Harrer, J., Ashour, R., Eberl, J.-M., Tolochko, P., & Boomgaarden, H. (2025). Beyond standardization: A comprehensive review of topic modeling validation methods for computational social science research. Political Science Research and Methods, 1–19. https://doi.org/10.1017/psrm.2025.10008
  3. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. J. Mach. Learn. Res., 3(null), 993–1022.
  4. Bou-Franch, P., Lorenzo-Dus, N., & Blitvich, P. G.-C. (2012). Social interaction in YouTube text-based polylogues: A study of coherence. Journal of Computer-Mediated Communication, 17(4), 501–521. https://doi.org/10.1111/j.1083-6101.2012.01579.x
  5. Bucher, T., & Helmond, A. (2018). The affordances of social media platforms. In J. Burgess, A. Marwick, & T. Poell, The SAGE Handbook of Social Media (pp. 233–253). SAGE Publications Ltd. https://doi.org/10.4135/9781473984066.n14
  6. Burgess, J., & Green, J. (2018). Youtube: Online video and participatory culture (2nd ed.). Polity Press.
  7. Cakmak, M. C., Agarwal, N., & Oni, R. (2024). The bias beneath: Analyzing drift in YouTube’s algorithmic recommendations. Social Network Analysis and Mining, 14(1), 171. https://doi.org/10.1007/s13278-024-01343-5
  8. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 4171–4186). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423
  9. Fang, Z., Alqazlan, L., Liu, D., He, Y., & Procter, R. (2023). A user-centered, interactive, human-in-the-loop topic modelling system. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 505–522. https://doi.org/10.18653/v1/2023.eacl-main.37
  10. Gainsbury, S. M. (2015). Online gambling addiction: The relationship between internet gambling and disordered gambling. Current Addiction Reports, 2(2), 185–193. https://doi.org/10.1007/s40429-015-0057-8
  11. Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press.
  12. Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure (arXiv:2203.05794). arXiv. https://doi.org/10.48550/arXiv.2203.05794
  13. Hoebanx, P., & French, M. (2023). Interpassive gambling: The case of slot machine vlogs on YouTube. Critical Gambling Studies, 4(1), 66–76. https://doi.org/10.29173/cgs150
  14. Hosseinmardi, H., Ghasemian, A., Rivera-Lanas, M., Horta Ribeiro, M., West, R., & Watts, D. J. (2024). Causally
  15. estimating the effect of YouTube’s recommender system using counterfactual bots. Proceedings of the National Academy of Sciences, 121(8), e2313377121. https://doi.org/10.1073/pnas.2313377121
  16. Hou, Y., & Huang, J. (2025). Natural language processing for social science research: A comprehensive review. Chinese Journal of Sociology, 11(1), 121–157. https://doi.org/10.1177/2057150X241306780
  17. İli, B. (2024). Çevrimiçi kumarın sosyal medyada tehlikeli yükselişi: YouTube videoları üzerine bir inceleme. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi, 12(2), 793–815. https://doi.org/10.19145/e- gifder.1407240
  18. James, R. J. E., & Bradley, A. (2021). The use of social media in research on gambling: A systematic review. Current Addiction Reports, 8(2), 235–245. https://doi.org/10.1007/s40429-021-00364-w
  19. Langham, E., Thorne, H., Browne, M., Donaldson, P., Rose, J., & Rockloff, M. (2015). Understanding gambling related harm: A proposed definition, conceptual framework, and taxonomy of harms. BMC Public Health, 16(1), 80. https://doi.org/10.1186/s12889-016-2747-0
  20. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J.,
  21. Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742
  22. Markham, A. (2012). Fabrication as ethical practice: Qualitative inquiry in ambiguous Internet contexts. Information, Communication & Society, 15(3), 334–353. https://doi.org/10.1080/1369118X.2011.641993
  23. Nelson, L. K. (2020). Computational grounded theory: A methodological framework. Sociological Methods & Research, 49(1), 3–42. https://doi.org/10.1177/0049124117729703
  24. Newall, P. (2025). Sludge, dark patterns and dark nudges: A taxonomy of online gambling platforms’ deceptive design features. Addiction, 120(10), 1916–1923. https://doi.org/10.1111/add.70085
  25. Ohme, J., Araujo, T., Boeschoten, L., Freelon, D., Ram, N., Reeves, B. B., & Robinson, T. N. (2024). Digital trace data collection for social media effects research: APIs, data donation, and (screen) tracking. Communication Methods and Measures, 18(2), 124–141. https://doi.org/10.1080/19312458.2023.2181319
  26. Papacharissi, Z. (2015). Affective publics: Sentiment, technology, and politics. Oxford University Press.
  27. Pitt, H., McCarthy, S., Randle, M., Daube, M., & Thomas, S. L. (2024). Young people’s views about the use of celebrities and social media influencers in gambling marketing. Health Promotion International, 39(1), daae012. https://doi.org/10.1093/heapro/daae012
  28. Reith, G. (2013). Techno economic systems and excessive consumption: A political economy of ‘pathological’ gambling. The British Journal of Sociology, 64(4), 717–738. https://doi.org/10.1111/1468-4446.12050
  29. Salganik, M. J. (2018). Bit by bit: Social research in the digital age. Princeton University Press.
  30. Schüll, N. D. (2012). Addiction by design: Machine gambling in Las Vegas. Princeton University Press. https://doi.org/10.2307/j.ctt12f4d0
  31. Schweter, S. (2020). BERTurk—BERT models for Turkish [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.3770924
  32. Singer, J. (2025). Stigmatisation of gambling disorder in social media: A tailored deep learning approach for YouTube comments. Harm Reduction Journal, 22(1), 56. https://doi.org/10.1186/s12954-025-01169-0
  33. Steinert, S., & Dennis, M. J. (2022). Emotions and digital well-being: On social media’s emotional affordances. Philosophy & Technology, 35(2), 36. https://doi.org/10.1007/s13347-022-00530-6
  34. Theocharis, Y., & Jungherr, A. (2021). Computational social science and the study of political communication. Political Communication, 38(1–2), 1–22. https://doi.org/10.1080/10584609.2020.1833121
  35. Townsend, L., & Wallace, C. (2016). Social media research: A guide to ethics. University of Aberdeen, 1(16), 1–16.
  36. van Baal, S. T., Bogdanski, P., Daryanani, A., Walasek, L., & Newall, P. (2025). The lived experience of gambling- related harm in natural language. Psychology of Addictive Behaviors, 39(4), 397–409. https://doi.org/10.1037/adb0001030
  37. van der Maas, M., & Samuel, J. (2025). Online gambling forums as a potential target for harm reduction: An exploratory natural language processing analysis of a reddit.com forum. Harm Reduction Journal, 22(1), 77. https://doi.org/10.1186/s12954-025-01220-0
  38. Wardle, H., Degenhardt, L., Marionneau, V., Reith, G., Livingstone, C., Sparrow, M., Tran, L. T., Biggar, B., Bunn, C.,
  39. Farrell, M., Kesaite, V., Poznyak, V., Quan, J., Rehm, J., Rintoul, A., Sharma, M., Shiffman, J., Siste, K., Ukhova, D., … Saxena, S. (2024). The Lancet Public Health Commission on gambling. The Lancet Public Health, 9(11), e950– e994. https://doi.org/10.1016/S2468-2667(24)00167-1
  40. Wardle, H., Reith, G., Langham, E., & Rogers, R. D. (2019). Gambling and public health: We need policy action to prevent harm. BMJ, l1807. https://doi.org/10.1136/bmj.l1807
  41. Yeşilay. (2025). Yeşilay Akademi raporları serisi Türkiye kumar raporu. Yeşilay Yayınları.