Understanding Online Expressions of Mental Illness: A Coherence-Driven Topic Modeling Approach to Reddit Psychiatric Texts via n-grams

Abd Rahman, R., Omar, K., Noah, S. A. M., Danuri, M. S. N. M., & Al-Garadi, M. A. (2020). Application of machine learning methods in mental health detection: A systematic review. IEEE Access, 8, 183952–183964.

Article  Google Scholar 

Agarwal, D., Singh, V., Singh, A. K., et al. (2024). Stacked ensemble model for analyzing mental health disorder from social media data. Multimedia Tools Applications, 83, 53923–53948. https://doi.org/10.1007/s11042-023-17395-2

Article  Google Scholar 

Akhmedov, F., Abdusalomov, A., Makhmudov, F., & Cho, Y. I. (2021). LDA-based topic modeling sentiment analysis using topic/document/sentence (TDS) model. Applied Sciences, 11, 1–15. https://doi.org/10.3390/app112311876

Article  Google Scholar 

Alattar, F., & Shaalan, K. (2021). Emerging research topic detection using filtered-LDA. AI, 2, 578–599. https://doi.org/10.3390/ai2040035

Article  Google Scholar 

American Psychiatric Association. (2025). What is autism spectrum disorder? Accessed 2025–04–20.

Angeler, D. G., Smith, E., Berk, M., et al. (2023). Navigating the multiple dimensions of the creativity-mental disorder link: A convergence mental health perspective. Discover Mental Health, 3, 24. https://doi.org/10.1007/s44192-023-00051-4

Article  PubMed  PubMed Central  Google Scholar 

Anupriya, P., & Karpagavalli, S. (2015) LDA based topic modeling of journal abstracts. In 2015 International Conference on Advanced Computing and Communication Systems (pp. 1–5). https://doi.org/10.1109/ICACCS.2015.7324058.

Asnawi, M. F., et al. (2024). Topic modelling analysis on Indonesian news using Bert topic model. In 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS) (pp. 1–6). https://doi.org/10.1109/ICORIS63540.2024.10903779.

Baeldung. (2025).When coherence score is good or bad in topic modeling? https://www.baeldung.com/cs/topic-modeling-coherence-score. Accessed 2025–04–21.

Blei, D., Ng, A., & Jordan, M. (2001). Latent Dirichlet allocation. <book-title update="added">Advances in Neural Information Processing Systems 14: Proceedings of the 2001 conference (Vol. 3, pp. 601–6080)

Google Scholar 

Bois, J. (2022) Dirichlet distribution. Accessed 2023–04–25.

Chancellor, S., & Choudhury, M. (2020). Methods in predictive techniques for mental health status on social media: A critical review. npj Digital Medicine. https://doi.org/10.1038/s41746-020-0233-7

Chang, I.-C., Yu, T.-K., Chang, Y.-J., & Yu, T.-Y. (2021). Applying text mining, clustering analysis, and latent Dirichlet allocation techniques for topic classification of environmental education journals. Sustainability, 13, 10856. https://doi.org/10.3390/su131910856

Article  Google Scholar 

Chang, J., Boyd-Graber, J., Gerrish, S., Wang, C., & Blei, D. (2009). Reading tea leaves: How humans interpret topic models, vol. 32. (pp. 288–296).

Coppersmith, G., Dredze, M., Harman, C., & Hollingshead, K. (2015). From ADHD to sad: Analyzing the language of mental health on twitter through self-reported diagnoses. 1–10. https://doi.org/10.3115/v1/W15-1201.

Clinic, M. (2025a). Schizophrenia: Symptoms and causes. https://www.mayoclinic.org/diseases-conditions/schizophrenia/symptoms-causes/syc-20354443. Accessed 2025–01–16.

Clinic, C. (2025b). Depression. https://my.clevelandclinic.org/health/diseases/9290-depression. Accessed 2025–01–16.

Clinic, M. (2025c). Bipolar disorder. https://www.mayoclinic.org/diseases-conditions/bipolar-disorder/symptoms-causes/syc-20355955. Accessed 2025–01–16.

DataReportal & Social, W. A. (2024) Number of internet and social media users worldwide as of January 2024 (in billions). Graph in Statista.

De Choudhury, M., Counts, S., & Horvitz, E. (2013) Major life changes and behavioral markers in social media: Case of childbirth. In Proceedings of the 2013 Conference on Computer Supported Cooperative Work. CSCW (vol. 13, pp. 1431–1442). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/2441776.2441937.

Didwania, K., Toshniwal, D., & Agarwal, A. (2023) Unveiling themes in judicial proceedings: A cross-country study using topic modeling on legal documents from India and the UK. Prepared or Unpublic Manuscript. To be updated with publication venue if available.

Douven, I., & Meijs, W. (2007). Measuring coherence. Synthese, 156, 405–425. https://doi.org/10.1007/s11229-006-9131-z

Article  Google Scholar 

Ezerceli, Ö., & Dehkharghani, R. (2024). Mental disorder and suicidal ideation detection from social media using deep neural networks. Journal of Computational Social Science, 7, 2277–2307. https://doi.org/10.1007/s42001-024-00307-1

Article  Google Scholar 

Garg, S. (2021) Topic modeling with LSA, PLSA, LDA and NMF: Bertopic and top2vec - a comparison. Accessed 10 May 2023.

Guo, Y., Zhang, Z., & Xu, X. (2023). Research on the detection model of mental illness of online forum users based on convolutional network. BMC Psychology, 11, 424. https://doi.org/10.1186/s40359-023-01460-4

Article  PubMed  PubMed Central  Google Scholar 

Gupta, R. K., et al. (2022). Prediction of research trends using LDA based topic modeling. Global Transitions Proceedings, 3, 298–304. https://doi.org/10.1016/j.gltp.2022.03.015

Article  Google Scholar 

Haldorai, A., Babitha Lincy, R., Murugan, S., & Balakrishnan, M. (2024). Deep learning for mental health disorder via social network analysis. Artificial Intelligence for Sustainable Development, EAI/Springer Innovations in Communication and Computing. Springer. https://doi.org/10.1007/978-3-031-53972-5_8

Hare, E., Joffe, E., Wilson, C., Serpell, J., & Otto, C. M. (2021). Behavior traits associated with career outcome in a prison puppy-raising program. Applied Animal Behaviour Science, 236, Article 105218. https://doi.org/10.1016/j.applanim.2021.105218

Article  Google Scholar 

Hasan, M., Rahman, A., Karim, M. R., Khan, M. S. I., & Islam, M. J. (2021) Normalized approach to find optimal number of topics in latent Dirichlet allocation (LDA). In Kaiser, M. S., Bandyopadhyay, A., Mahmud, M. & Ray, K. (Eds.) Proceedings of International Conference on Trends in Computational and Cognitive Engineering (pp. 341–354). Springer Singapore, Singapore.

Herqutanto, M. F., Putra Zatari, R., & Sutoyo, R. (2023). Topic modeling using LDA-based and machine learning for aspect sentiment analysis. In 2023 International Conference on Informatics, Multimedia, Cyber and Informations System (ICIMCIS), Jakarta Selatan, Indonesia (pp. 142–148). https://doi.org/10.1109/ICIMCIS60089.2023.10349056

Hridoy, M. T. A., Saha, S. R., Islam, M. M., et al. (2024). Leveraging web scraping and stacking ensemble machine learning techniques to enhance detection of major depressive disorder from social media posts. Social Network Analysis and Mining, 14, Article 239. https://doi.org/10.1007/s13278-024-01392-w

Article  Google Scholar 

Huang, A.-L. (2008). Similarity measures for text document clustering. Technical Report, University of Waikato.

Islam, M. R., et al. (2018). Depression detection from social network data using machine learning techniques. Health Information Science and Systems, 6, Article 8. https://doi.org/10.1007/s13755-018-0046-0

Article  PubMed  PubMed Central  Google Scholar 

Jelodar, H., Wang, Y., Yuan, C., & Feng, X. (2017). Latent Dirichlet allocation (LDA) and topic modeling: Models, applications, a survey. CoRR abs/1711.04305.1711.04305.

Joshi, D. J., Makhija, M., Nabar, Y., Nehete, N. & Patwardhan, M. S. (2018). Mental health analysis using deep learning for feature extraction. In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. pp. 356–359.

Juluru, K., Shih, H.-H., Murthy, K. N. K., & Elnajjar, P. (2021). Bag-of-words technique in natural language processing: A primer for radiologists. Radiographics, 41, 1420–1426. https://doi.org/10.1148/rg.2021210013

Article  PubMed  Google Scholar 

Kanaparthi, S. D., Patle, A., & Naik, K. J. (2023). Prediction and detection of emotional tone in online social media mental disorder groups using regression and recurrent neural networks. Multimedia Tools and Applications, 82, 43819–43839. https://doi.org/10.1007/s11042-023-15316-x

Article  Google Scholar 

Kerasiotis, M., Ilias, L., & Askounis, D. (2024). Depression detection in social media posts using transformer-based models and auxiliary features. Social Network Analysis and Mining, 14, 196. https://doi.org/10.1007/s13278-024-01360-4

Article  Google Scholar 

Kim, J., Lee, J., Park, E., & Han, J. (2020). A deep learning model for detecting mental illness from user content on social media. Scientific Reports, 10, Article 11846. https://doi.org/10.1038/s41598-020-68764-y

Article  CAS  PubMed  PubMed Central  Google Scholar 

Khan, A., & Ali, R. (2023). Measuring the effectiveness of LDA-based clustering for social media data. In 2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS) (pp. 1–8). https://doi.org/10.1109/AICAPS57044.2023.100743990

Kodati, D., & Tene, R. (2024). Advancing mental health detection in texts via multi-task learning with soft-parameter sharing transformers. Neural Computing & Applications. https://doi.org/10.1007/s00521-024-10753-7

Article  Google Scholar 

Kohli,P.P.S. (2021) Standard metrics for LDA model comparison. https://pahulpreet86.github.io/standard-metrics-for-lda-model-comparison/. Accessed 2025–04–21.

Koltsov, S., Nikolenko, S. I., Koltsova, O., & Bodrunova, S. (2016). Stable topic modeling for web science: Granulated LDA. In Proceedings of the 8th ACM Conference on Web Science (pp. 85–94). https://doi.org/10.1145/2908131.2908184 (Association for Computing Machinery).

Kurian, R. S., Chaudhary, C., Nambiar, A. U., & Sunny, A. (2025). Metan: Metaphoric temporal attention network for depression detection on social media. In Barhamgi, M., Wang, H. & Wang, X. (Eds.), Web Information Systems Engineering – WISE 2024, vol. 15437 of Lecture Notes in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-96-0567-5_8.

Liu, Y., Du, F., Sun, J., & Jiang, Y. (2020). Ilda: An interactive latent Dirichlet allocation model to improve topic quality. Journal of Information Science, 46, 3–17. https://doi.org/10.1177/0165551518822455

Article  Google Scholar 

Liu, Y. (2024). Depression detection via a Chinese social media platform: A novel causal relation-aware deep learning approach. The Journal of Supercomputing, 80, 10327–10356. https://doi.org/10.1007/s11227-023-05830-y

Article  Google Scholar 

Ma, J., Wang, L., Zhang, Y.-R., Yuan, W., & Guo, W. (2023). An integrated latent Dirichlet allocation and word2vec method for generating the topic evolution of mental models from global to local. Expert Systems with Applications, 212, Article 118695. https://doi.org/10.1016/j.eswa.2022.118695

Article  Google Scholar 

McInnes, L., & Healy, J. (2017) Accelerated hierarchical density based clustering. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 33–42). IEEE. https://doi.org/10.1109/ICDMW.2017.12

MedlinePlus. Anxiety. (2025). Accessed 2025–01–16.

National Institute of Mental Health. (2025). Borderline personality disorder. Accessed 2025–01–16.

O’Dea, B., et al. (2015). Detecting Suicidality on Twitter. Internet Interv, 2, 183–188. https://doi.org/10.1016/j.invent.2015.03.005

Article  Google Scholar 

Redzuan, N., Möller, R., Gehrke, M., & Braun, T. (2023). On domain-specific topic modelling using the case of a humanities journal. In Proceedings of the Workshop on Humanities-Centred Artificial Intelligence, vol. 3580 of CEUR Workshop Proceedings (pp. 25–37). CEUR-WS.org, Aachen, Germany. Co-located with the 46th German Conference on Artificial Intelligence (KI).

Shen, T., et al. (2018) Cross-domain depression detection via harvesting social media. 1611–1617. https://doi.org/10.24963/ijcai.2018/223.

Sik, D., Németh, R., & Katona, E. (2021). Topic modelling online depression forums: Beyond narratives of self-objectification and self-blaming. Journal of Mental Health. https://doi.org/10.1080/09638237.2021.1979493

Article  PubMed  Google Scholar 

Comments (0)

No login
gif