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Abbe A, Falissard B. Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach. JMIR Ment Health. 2017;4(4):e48doi: 10.2196/mental.7797.
Abbe, A., & Falissard, B. (2017). Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach. JMIR mental health, 4(4), e48. https://doi.org/10.2196/mental.7797
Abbe, Adeline, and Falissard, Bruno. "Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach." JMIR mental health vol. 4,4 (2017): e48. doi: https://doi.org/10.2196/mental.7797
Abbe A, Falissard B. Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach. JMIR Ment Health. 2017 Oct 23;4(4):e48. doi: 10.2196/mental.7797. PMID: 29061554; PMCID: PMC5673886.
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JMIR Ment Health. 2017 Oct 23;4(4):e48. doi: 10.2196/mental.7797.

Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach.

JMIR mental health

Adeline Abbe, Bruno Falissard

Affiliations

  1. Centre de recherche en épidémiologie et santé des populations (CESP)/ Institut national de la santé et de la recherche médicale (INSERM) U1018, Maison de Solenn, Paris cedex 14, France.

PMID: 29061554 PMCID: PMC5673886 DOI: 10.2196/mental.7797

Abstract

BACKGROUND: Internet is a particularly dynamic way to quickly capture the perceptions of a population in real time. Complementary to traditional face-to-face communication, online social networks help patients to improve self-esteem and self-help.

OBJECTIVE: The aim of this study was to use text mining on material from an online forum exploring patients' concerns about treatment (antidepressants and anxiolytics).

METHODS: Concerns about treatment were collected from discussion titles in patients' online community related to antidepressants and anxiolytics. To examine the content of these titles automatically, we used text mining methods, such as word frequency in a document-term matrix and co-occurrence of words using a network analysis. It was thus possible to identify topics discussed on the forum.

RESULTS: The forum included 2415 discussions on antidepressants and anxiolytics over a period of 3 years. After a preprocessing step, the text mining algorithm identified the 99 most frequently occurring words in titles, among which were escitalopram, withdrawal, antidepressant, venlafaxine, paroxetine, and effect. Patients' concerns were related to antidepressant withdrawal, the need to share experience about symptoms, effects, and questions on weight gain with some drugs.

CONCLUSIONS: Patients' expression on the Internet is a potential additional resource in addressing patients' concerns about treatment. Patient profiles are close to that of patients treated in psychiatry.

©Adeline Abbe, Bruno Falissard. Originally published in JMIR Mental Health (http://mental.jmir.org), 23.10.2017.

Keywords: antidepressant; anxiolytic; data mining; social media; text mining

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