Discursive framing of depression in online health communities

Depression is a disease of modernity, where societies impose increased responsibility on the individual, while the individual does not have the opportunity to change his or her circumstances (Sik 2018). In this sense, the problem of depression is embedded into the more general problem of the distortion of social integration.
A current question in sociology is how mental disorders are framed by health professionals and by the patients themselves. A related questions is how psychotherapists transform social suffering into suffering related to the self (see e.g. Flick, 2016).

Previous research in this field has been primarily qualitative. Investigators have used qualitative content analysis of offline texts (personal diaries, letters, interviews) to investigate the framing of depression (e.g. Riskind et al, 1989). We believe that there is significant research utility in the application of automated text analysis methods to investigate the framing of depression in online, patient-generated non-clinical texts.

We investigate the potential for NLP techniques in understanding individual framing of depression in online health communities. Framing of depression is a social construction, it defines the meaning of depression, gives a causal explanation of it and can even determine treatment preferences. The current clinical explanations of depression point to biological, psychological and social discourses (e.g. Comer, 2015).

Forum posts are classified into three framing types by applying different supervised learning algorithms, then distribution and mixture patterns of framing types, their influencing contextual/linguistic/topical factors, and dynamics of these features are examined. We addressed the following questions: How are the three main types of framing distributed? In what pattern are they mixed with each other? What contextual factors (type of forum, communicative behavior of author etc.) influence which framing type is utilized?

Related Results

Németh, Renáta; Sik, Domonkos; Zaboretzky, Bendegúz and Katona, Eszter (2023): Depression in times of a pandemic – the impact of COVID-19 on the lay discourses of e-mental health communities

2023.06.15. Publication

The article by Renáta Németh, Domonkos Sik, Bendegúz Zaboretzky and Eszter Katona entitled "Depression in times of a pandemic - the impact of COVID-19 on the lay discourses of e-mental health communities" was published in the journal Information, Communication & Society.

View Result Details

ELTE-TINLAB workshop

2022.06.27. Presentation

On 22nd June our ELTE-TINLAB research team hosted a workshop titled 'Online lay depression discourses – research summary and recommendations by the ELTE-TINLAB depression forum research team', inviting professionals involved in health care and e-mental health. Our research last semester was based on two questions: How are the online depression [...]

View Result Details

Fanni Máté: Examination of the framing modes of depression on online forums with natural language processing

2022.01.24. Publication

One of our researchers, Fanni Máté has gotten her paper published in the special issue of the journal Metszetek. Fanni applies logistical regression to map out depression’s interpretative frames in non-professional discourse.

View Result Details

Németh, Renáta; Máté, Fanni; Katona, Eszter; Rakovics, Márton; Sik, Domonkos (2022): Bio, psycho, or social: supervised machine learning to classify discursive framing of depression in online health communities.

2022.01.08. Publication

Renáta Németh, Fanni Máté, Eszter Katona, Márton Rakovics and Domonkos Sik published their results in the journal Quality and Quantity: International Journal of Methodology with the following title: " Bio, psycho, or social: supervised machine learning to classify discursive framing of depression in online health communities ".

View Result Details

Eszter Katona, Domonkos Sik, Renáta Németh (2021): Automated text analysis of topics represented on depression forums using the biopsychosocial model of depression

2021.12.14. Publication

The Journal Egészségfejlesztés requested a short report for Hungarian professionals about the results of our research series in the topic of online depression forums.

View Result Details

Domonkos Sik (2021): Empty Suffering. A Social Phenomenology of Depression, Anxiety and Addiction

2021.12.08. Publication

Domonkos Sik, our team member, has just published a new book by Routledge summarizing ten years of research on social suffering: https://www.routledge.com/Empty-Suffering-A-Social-Phenomenology-of-Depression-Anxiety-and-Addiction/Sik/p/book/9781032045573 Domonkos leads our research team's analysis of online depression forums: https://rc2s2.elte.hu/en/project/discursive-framing-of-depression-in-online-health-communities/

View Result Details

Domonkos Sik, Renáta Németh, Eszter Katona: Topic modelling online depression forums: beyond narratives of self-objectification and self-blaming

2021.09.29. Publication

A publication of our research group has been published by Journal of Mental Health (IF 4.3). If you don’t have access to T&F, feel free to use one of the 50 free online copies: https://www.tandfonline.com/eprint/AFFUU26JFMIB7BNXATAZ/full?target=10.1080/09638237.2021.1979493

View Result Details

Fanni Máté (2021): Social Support on an Online Forum for Depression and Anxiety

2021.06.13. Publication

Nowadays, online communities are typical sources of social support, which is a considerable help especially for those suffering from depression or anxiety. The aim of my research is to investigate the patterns of social support on an online depression and anxiety forum and to serve as an exploratory research of [...]

View Result Details

Németh, Sik, Katona (2021) – The asymmetries of the biopsychosocial model of depression in lay discourses – Topic modelling online depression forums

2021.04.26. Publication

New results of our project ‘NLP analysis of online depression forums’ was published in SSM Population Health (D1) written by Renáta Németh, Domonkos Sik and Eszter Katona. The asymmetries of the biopsychosocial model of depression in lay discourses - Topic modeling of online depression forums.

View Result Details

Our former publications in related topics

2021.04.14. Publication

Sik Domonkos: From mental disorders to social suffering: Making sense of depression for critical theories. EUROPEAN JOURNAL OF SOCIAL THEORY (2018) Sik, Domonkos: Válaszok a szenvedésre: A hálózati szolidaritás elmélete. Budapest, Magyarország : ELTE Eötvös Kiadó (2018) , 228 p. Sik, Domonkos: A szenvedés határállapotai: Egy kritikai hálózatelmélet vázlata. Budapest, [...]

View Result Details

Sik, Domonkos (2020): From Lay Depression Narratives to Secular Ritual Healing: An Online Ethnography of Mental Health Forums

2020.12.29. Publication

The article aims at analysing online depression forums enabling lay reinterpretation and criticism of expert biomedical discourses. Firstly, two contrasting interpretations of depression are reconstructed: expert psy-discourses are confronted with the phenomenological descriptions of lay experiences, with a special emphasis on online forums as empirical platforms hosting such debates. After [...]

View Result Details

Renáta Németh, Domonkos Sik, Fanni Máté. 2020. “Machine learning of concepts hard even for humans: the case of online depression forums”. International Journal of Qualitative Methods

2020.08.25. Publication

Social scientists of mixed-methods research have traditionally used human annotators to classify texts according to some predefined knowledge. The ‘big data’ revolution, the fast growth of digitized texts in recent years brings new opportunities but also new challenges. In our research project, we aim to examine the potential for natural [...]

View Result Details

Bio, psycho or social – Discursive framing of depression in online health communities – IC2S2, 5th International Conference on Computational Social Science, Amsterdam, 2019

2019.07.17. Presentation

In our research we aimed at gathering and automatically classifying online forum posts into the above three framing types by applying different supervised learning algorithms. As our dataset, we decided to use depression-related posts from the most popular English-speaking health forums within the time interval 2016-2018. We obtained only publicly [...]

View Result Details