ArticleJMIR mental health2022
Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study.
Article in JMIR mental health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- What methods are used to examine representation of mental ill-health on social media? A systematic review.BMC psychology · 2024Pooled it
- Sleep Across the Pandemic in Norwegian University and College Students: A National Repeated Cross-Sectional Analysis (2010-2023).Journal of sleep research · 2026Article
- A machine learning language model approach to evaluating mental health awareness content across Spanish- and English-language social media posts on Twitter.Social psychiatry and psychiatric epidemiology · 2025Article
- Stigma Attitudes Toward HIV/AIDS From 2011 Through 2023 in Japan: Retrospective Study in Japan.Journal of medical Internet research · 2025Article
- Wellness Influencer Responses to COVID-19 Vaccines on Social Media: A Longitudinal Observational Study.Journal of medical Internet research · 2024Observational
- Reliability and validity of the Chinese version of the doomscrolling scale and the mediating role of doomscrolling in the bidirectional relationship between insomnia and depression.BMC psychiatry · 2024Article
- A Scoping Review of Physician Advocacy on Twitter.Cureus · 2023Article
- The Willingness to Utilize Psychological Health Applications Among Saudis: A Study Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT).Inquiry : a journal of medical care organization, provision and financingArticle
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMental health challenges are thought to affect approximately 10% of the global population each year, with many of those affected going untreated because of the stigma and limited access to services. As social media lowers the barrier for joining difficult conversations and finding supportive groups, Twitter is an open source of language data describing the changing experience of a stigmatized group.
objectiveBy measuring changes in the conversation around mental health on Twitter, we aim to quantify the hypothesized increase in discussions and awareness of the topic as well as the corresponding reduction in stigma around mental health.
methodsWe explored trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of approximately 10% of all English tweets from 2010 to 2021. We examined temporal dynamics of mental health language and measured levels of positivity of the messages. Finally, we used the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that was due to social amplification.
resultsWe found that the popularity of the phrase mental health increased by nearly two orders of magnitude between 2012 and 2018. We observed that mentions of mental health spiked annually and reliably because of mental health awareness campaigns as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional television stories portraying suicide. We found that the level of positivity of messages containing mental health, while stable through the growth period, has declined recently. Finally, we observed that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that the stigma associated with the discussion of mental health on Twitter has diminished with time.
conclusionsThese results provide useful texture regarding the growing conversation around mental health on Twitter and suggest that more awareness and acceptance has been brought to the topic compared with past years.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.