A Corpus-Based Study of Adjectives and Collocates in Reddit Posts on Anxiety and Depression
Main Article Content
Keywords
Anxiety, Depression, Adjective, Collocate, Emotional Meaning
Abstract
Talking openly about anxiety and depression (A&D) remains difficult for many people because of the stigma surrounding mental illness. Anonymous online platforms such as Reddit provide a space where users can express their thoughts and emotions more freely. This study considers how individuals linguistically construct and intensify emotional distress by examining (1) the adjectives used to express A&D, (2) the content-word collocates that co-occur with these adjectives, (3) the lexical features of these collocates, and (4) the emotional meanings conveyed through these collocational patterns. The dataset consisted of 1,440 Reddit posts (approximately 300,000 tokens) systematically sampled from the r/Anxiety and r/Depression subreddits between 2023 and 2025. An observational mixed-methods corpus linguistic approach was used to examine the data. Quantitative corpus linguistic analyses were carried out using AntConc, including frequency profiling and Mutual Information (MI) analysis, and were enhanced by qualitative concordance and Keyword-in-Context (KWIC) analysis to examine collocational patterns in context. The analysis shows a predominance of negatively valenced adjectives (e.g., anxious, depressed, hopeless, and suicidal), whose meanings are systematically intensified through their collocational environments. The collocates show distinctive lexical features. These include clinical nouns, linking and change-of-state verbs, and degree and frequency adverbs. These features construct varying levels of affective intensity and psychological distress. Emotional meaning is encoded in recurrent collocational patterns. Individual lexical items reveal only part of this meaning. This shows the value of collocational analysis for digital mental health research. The findings also possess practical implications. They may help improve the diagnostic sensitivity of automated digital mental health tools and foster more empathetic clinical communication.
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References
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Knaevelsrud, C., & Maercker, A. (2007). Internet-based treatment for PTSD reduces distress and facilitates the development of a strong therapeutic alliance: A randomized controlled clinical trial. BMC Psychiatry, 7(1), Article 13. https://doi.org/10.1186/1471-244X-7-13
Kovacs, M., & Beck, A. T. (1978). Maladaptive cognitive structures in depression. American Journal of Psychiatry, 135(5), 525–533. https://doi.org/10.1176/ajp.135.5.525
Low, D. M., Rumker, L., Talkar, T., Torous, J., Cecchi, G., & Ghosh, S. (2020). Natural language processing reveals vulnerable mental health support groups and heightened health anxiety on Reddit during COVID-19: Observational study. Journal of Medical Internet Research, 22(10), e22635. https://doi.org/10.2196/22635
MacLeod, C., & Cohen, I. L. (1993). Anxiety and the interpretation of ambiguity: A text comprehension study. Journal of Abnormal Psychology, 102(2), 238–247. https://doi.org/10.1037/0021-843x.102.2.238
Mayopoulos, G., & Farber, B. A. (2023). Disclosure in psychotherapy versus in anonymous and non-anonymous online spaces. Psychotherapy Research, 34(5), 638–647. https://doi.org/10.1080/10503307.2023.2256954
Morris, R. R., Schueller, S. M., & Picard, R. W. (2015). Efficacy of a web-based, crowdsourced peer-to-peer cognitive reappraisal platform for depression: Randomized controlled trial. Journal of Medical Internet Research, 17(3), e72. https://doi.org/10.2196/jmir.4167
Nguyen, K. T. P., Vu, B., Chandna, S., Schultz, J., & Mayer, G. (2025). Between the lines: Investigating health beliefs and emotional expressions in online mental health communities. Frontiers in Psychology, 16, Article 1521623. https://doi.org/10.3389/fpsyg.2025.1521623
Nguyen, T. Q., Yates, A., Zirikly, A., Desmet, B., & Cohan, A. (2022). Improving the generalizability of depression detection by leveraging clinical questionnaires. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 8446–8459). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.acl-long.578
O’Dea, B., Batterham, P. J., Braund, T. A., Chakouch, C., Larsen, M., Berk, M., Torok, M., Christensen, H., & Glozier, N. (2025). A randomised cross over trial examining the linguistic markers of depression and anxiety in symptomatic adults. npj Mental Health Research, 4(1), Article 30. https://doi.org/10.1038/s44184-025-00140-y
Pavalanathan, U., & De Choudhury, M. (2015). Identity management and mental health discourse in social media. In Proceedings of the 24th International Conference on World Wide Web (pp. 315–321). Association for Computing Machinery. https://doi.org/10.1145/2740908.2743049
Prescott, J., Hanley, T., & Ujhelyi, K. (2017). Peer communication in online mental health forums for young people: Directional and nondirectional support. JMIR Mental Health, 4(3), e29. https://doi.org/10.2196/mental.6921
Quillivic, R., Gayraud, F., Auxéméry, Y., Vanni, L., Peschanski, D., Eustache, F., Dayan, J., & Mesmoudi, S. (2023). Towards unlocking the linguistic code of post-traumatic stress disorder: A comprehensive analysis and diagnostic approach [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-3747882/v1
Robertson, A., Aiello, L. M., & Quercia, D. (2019). The language of dialogue is complex. Proceedings of the International AAAI Conference on Web and Social Media, 13(1), 428–439. https://doi.org/10.1609/icwsm.v13i01.3241
Robertson, C., Carney, J., & Trudell, S. (2023). Language about the future on social media as a novel marker of anxiety and depression: A big-data and experimental analysis. Current Research in Behavioral Sciences, 4, Article 100104. https://doi.org/10.1016/j.crbeha.2023.100104
Salekin, A., Eberle, J. W., Glenn, J. J., Teachman, B. A., & Stankovic, J. A. (2018). A weakly supervised learning framework for detecting social anxiety and depression. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(2), 1–26. https://doi.org/10.1145/3214284
Schnyder, N., Panczak, R., Groth, N., & Schultze-Lutter, F. (2017). Association between mental health-related stigma and active help-seeking: Systematic review and meta-analysis. The British Journal of Psychiatry, 210(4), 261–268. https://doi.org/10.1192/bjp.bp.116.189464
Soriano, C. (2013). Linguistic theories of lexical meaning. In J. F. R. Fontaine, K. R. Scherer, & C. Soriano (Eds.), Components of emotional meaning: A sourcebook (pp. 63–80). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199592746.003.0005
Suler, J. R. (2004). The online disinhibition effect. CyberPsychology & Behavior, 7(3), 321–326. https://doi.org/10.1089/1094931041291295
World Health Organization. (2022). World mental health report: Transforming mental health for all. https://www.who.int/publications/i/item/9789240049338
Xiao, Y., Baes, N., Vylomova, E., & Haslam, N. (2023). Have the concepts of ‘anxiety’ and ‘depression’ been normalized or pathologized? A corpus study of historical semantic change. PLOS ONE, 18(6), e0288027. https://doi.org/10.1371/journal.pone.0288027
Zarate, D., Ball, M., Prokofieva, M., Kostakos, V., & Stavropoulos, V. (2023). Identifying self-disclosed anxiety on Twitter: A natural language processing approach. Psychiatry Research, 330, Article 115579. https://doi.org/10.1016/j.psychres.2023.115579
Zhang, T., Schoene, A. M., Ji, S., & Ananiadou, S. (2022). Natural language processing applied to mental illness detection: A narrative review. npj Digital Medicine, 5(1), Article 89. https://doi.org/10.1038/s41746-022-00589-7
Almeida, A., Patton, T., Conway, M., Gupta, A., Strathdee, S. A., & Bórquez, A. (2024). The use of natural language processing methods in Reddit to investigate opioid use: Scoping review. JMIR Infodemiology, 4, e51156. https://doi.org/10.2196/51156
Anthony, L. (2022). What can corpus software do? In The Routledge handbook of corpus linguistics (pp. 103–125). Routledge. https://doi.org/10.4324/9780367076399-9
Baumel, A., Fleming, T., & Schueller, S. M. (2020). Digital micro interventions for behavioral and mental health gains: Core components and conceptualization of digital micro intervention care. Journal of Medical Internet Research, 22(10), e20631. https://doi.org/10.2196/20631
Béligon, S. (2020). Feeling, emotion and the company they keep: What adjectives reveal about the substantives feeling and emotion. Lexis, 15. https://doi.org/10.4000/lexis.4322
Boettcher, N. (2021). Studies of depression and anxiety using Reddit as a data source: Scoping review. JMIR Mental Health, 8(11), e29487. https://doi.org/10.2196/29487
Brody, D. J., & Hughes, J. P. (2025). Depression prevalence in adolescents and adults: United States, August 2021–August 2023 (NCHS Data Brief No. 527). National Center for Health Statistics. https://doi.org/10.15620/cdc/174579
Clark, D. A., & Beck, A. T. (2009). Cognitive therapy of anxiety disorders: Science and practice. Guilford Press.
Collins, L., & Baker, P. (2023). Language, discourse and anxiety. Cambridge University Press. https://doi.org/10.1017/9781009250139
Coppersmith, G., Dredze, M., & Harman, C. (2014). Quantifying mental health signals in Twitter. In Proceedings of the workshop on computational linguistics and clinical psychology: From linguistic signal to clinical reality (pp. 51–60). Association for Computational Linguistics. https://doi.org/10.3115/v1/w14-3207
De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. Proceedings of the International AAAI Conference on Web and Social Media, 7(1), 128–137. https://doi.org/10.1609/icwsm.v7i1.14432
Edo Marzá, N. (2011). A comprehensive corpus-based study of the use of evaluative adjectives in promotional hotel websites. ODISEA: Revista de Estudios Ingleses, 12, 97–123. https://doi.org/10.25115/odisea.v0i12.222
Everaert, J., Bronstein, M. V., Castro, A. A., Cannon, T. D., & Joormann, J. (2020). When negative interpretations persist, positive emotions don’t! Inflexible negative interpretations encourage depression and social anxiety by dampening positive emotions. Behaviour Research and Therapy, 124, Article 103510. https://doi.org/10.1016/j.brat.2019.103510
García-Méndez, S., de Arriba-Pérez, F., Beiro-Suso, J., & González-Castaño, F. J. (2026). Real-time anxiety and depression detection by combining large language models and machine learning with explainability capabilities on a user-centric, engaging conversational assistant. Expert Systems, 43(4), e70229. https://doi.org/10.1111/exsy.70229
Gonda, X., Pompili, M., Serafini, G., Carvalho, A. F., Rihmer, Z., & Döme, P. (2015). The role of cognitive dysfunction in the symptoms and remission from depression. Annals of General Psychiatry, 14(1), Article 27. https://doi.org/10.1186/s12991-015-0068-9
Gotlib, I. H., & Joormann, J. (2010). Cognition and depression: Current status and future directions. Annual Review of Clinical Psychology, 6(1), 285–312. https://doi.org/10.1146/annurev.clinpsy.121208.131305
Grub, M. F. (2025). Reddit as a “safe space”: Topic modeling of online mental health communities for depression and anxiety. Weizenbaum Journal of the Digital Society, 5(3). https://doi.org/10.34669/wi.wjds/5.3.3
Habicht, J., Viswanathan, S., Carrington, B., Hauser, T. U., Harper, R., & Rollwage, M. (2024). Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot. Nature Medicine, 30(2), 595–602. https://doi.org/10.1038/s41591-023-02766-x
Hunston, S. (2010). Corpus approaches to evaluation. Routledge. https://doi.org/10.4324/9780203841686
Kerz, E., Zanwar, S., Qiao, Y., & Wiechmann, D. (2023). Toward explainable AI (XAI) for mental health detection based on language behavior. Frontiers in Psychiatry, 14, Article 1219479. https://doi.org/10.3389/fpsyt.2023.1219479
Kim, S., Cha, J., Kim, D., & Park, E. (2023). Understanding mental health issues in different subdomains of social networking services: Computational analysis of text-based Reddit posts. Journal of Medical Internet Research, 25, e49074. https://doi.org/10.2196/49074
Knaevelsrud, C., & Maercker, A. (2007). Internet-based treatment for PTSD reduces distress and facilitates the development of a strong therapeutic alliance: A randomized controlled clinical trial. BMC Psychiatry, 7(1), Article 13. https://doi.org/10.1186/1471-244X-7-13
Kovacs, M., & Beck, A. T. (1978). Maladaptive cognitive structures in depression. American Journal of Psychiatry, 135(5), 525–533. https://doi.org/10.1176/ajp.135.5.525
Low, D. M., Rumker, L., Talkar, T., Torous, J., Cecchi, G., & Ghosh, S. (2020). Natural language processing reveals vulnerable mental health support groups and heightened health anxiety on Reddit during COVID-19: Observational study. Journal of Medical Internet Research, 22(10), e22635. https://doi.org/10.2196/22635
MacLeod, C., & Cohen, I. L. (1993). Anxiety and the interpretation of ambiguity: A text comprehension study. Journal of Abnormal Psychology, 102(2), 238–247. https://doi.org/10.1037/0021-843x.102.2.238
Mayopoulos, G., & Farber, B. A. (2023). Disclosure in psychotherapy versus in anonymous and non-anonymous online spaces. Psychotherapy Research, 34(5), 638–647. https://doi.org/10.1080/10503307.2023.2256954
Morris, R. R., Schueller, S. M., & Picard, R. W. (2015). Efficacy of a web-based, crowdsourced peer-to-peer cognitive reappraisal platform for depression: Randomized controlled trial. Journal of Medical Internet Research, 17(3), e72. https://doi.org/10.2196/jmir.4167
Nguyen, K. T. P., Vu, B., Chandna, S., Schultz, J., & Mayer, G. (2025). Between the lines: Investigating health beliefs and emotional expressions in online mental health communities. Frontiers in Psychology, 16, Article 1521623. https://doi.org/10.3389/fpsyg.2025.1521623
Nguyen, T. Q., Yates, A., Zirikly, A., Desmet, B., & Cohan, A. (2022). Improving the generalizability of depression detection by leveraging clinical questionnaires. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 8446–8459). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.acl-long.578
O’Dea, B., Batterham, P. J., Braund, T. A., Chakouch, C., Larsen, M., Berk, M., Torok, M., Christensen, H., & Glozier, N. (2025). A randomised cross over trial examining the linguistic markers of depression and anxiety in symptomatic adults. npj Mental Health Research, 4(1), Article 30. https://doi.org/10.1038/s44184-025-00140-y
Pavalanathan, U., & De Choudhury, M. (2015). Identity management and mental health discourse in social media. In Proceedings of the 24th International Conference on World Wide Web (pp. 315–321). Association for Computing Machinery. https://doi.org/10.1145/2740908.2743049
Prescott, J., Hanley, T., & Ujhelyi, K. (2017). Peer communication in online mental health forums for young people: Directional and nondirectional support. JMIR Mental Health, 4(3), e29. https://doi.org/10.2196/mental.6921
Quillivic, R., Gayraud, F., Auxéméry, Y., Vanni, L., Peschanski, D., Eustache, F., Dayan, J., & Mesmoudi, S. (2023). Towards unlocking the linguistic code of post-traumatic stress disorder: A comprehensive analysis and diagnostic approach [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-3747882/v1
Robertson, A., Aiello, L. M., & Quercia, D. (2019). The language of dialogue is complex. Proceedings of the International AAAI Conference on Web and Social Media, 13(1), 428–439. https://doi.org/10.1609/icwsm.v13i01.3241
Robertson, C., Carney, J., & Trudell, S. (2023). Language about the future on social media as a novel marker of anxiety and depression: A big-data and experimental analysis. Current Research in Behavioral Sciences, 4, Article 100104. https://doi.org/10.1016/j.crbeha.2023.100104
Salekin, A., Eberle, J. W., Glenn, J. J., Teachman, B. A., & Stankovic, J. A. (2018). A weakly supervised learning framework for detecting social anxiety and depression. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(2), 1–26. https://doi.org/10.1145/3214284
Schnyder, N., Panczak, R., Groth, N., & Schultze-Lutter, F. (2017). Association between mental health-related stigma and active help-seeking: Systematic review and meta-analysis. The British Journal of Psychiatry, 210(4), 261–268. https://doi.org/10.1192/bjp.bp.116.189464
Soriano, C. (2013). Linguistic theories of lexical meaning. In J. F. R. Fontaine, K. R. Scherer, & C. Soriano (Eds.), Components of emotional meaning: A sourcebook (pp. 63–80). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199592746.003.0005
Suler, J. R. (2004). The online disinhibition effect. CyberPsychology & Behavior, 7(3), 321–326. https://doi.org/10.1089/1094931041291295
World Health Organization. (2022). World mental health report: Transforming mental health for all. https://www.who.int/publications/i/item/9789240049338
Xiao, Y., Baes, N., Vylomova, E., & Haslam, N. (2023). Have the concepts of ‘anxiety’ and ‘depression’ been normalized or pathologized? A corpus study of historical semantic change. PLOS ONE, 18(6), e0288027. https://doi.org/10.1371/journal.pone.0288027
Zarate, D., Ball, M., Prokofieva, M., Kostakos, V., & Stavropoulos, V. (2023). Identifying self-disclosed anxiety on Twitter: A natural language processing approach. Psychiatry Research, 330, Article 115579. https://doi.org/10.1016/j.psychres.2023.115579
Zhang, T., Schoene, A. M., Ji, S., & Ananiadou, S. (2022). Natural language processing applied to mental illness detection: A narrative review. npj Digital Medicine, 5(1), Article 89. https://doi.org/10.1038/s41746-022-00589-7
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Jul 27, 2026
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Yuhan , Y., Lau, S. K., Handoko, H., Yap, L. L., & Kaur, S. (2026). A Corpus-Based Study of Adjectives and Collocates in Reddit Posts on Anxiety and Depression. Jurnal Arbitrer, 13(2), 145–160. https://doi.org/10.25077/ar.13.2.145-160.2026
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