ArticleJMIR mental health2019
Public Opinions on Using Social Media Content to Identify Users With Depression and Target Mental Health Care Advertising: Mixed Methods Survey.
Article in JMIR mental health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- Methodologies for Monitoring Mental Health on Twitter: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Toward an Ethical Framework for the Text Mining of Social Media for Health Research: A Systematic Review.Frontiers in digital health · 2020Pooled it
- AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges.Journal of medical Internet research · 2024Review
- A systematic review on automated clinical depression diagnosis.Npj mental health research · 2023Review
- Ethical and legal considerations in social media research for health technology assessment: conclusions from a scoping review.International journal of technology assessment in health care · 2023Article
- Screen Media Use Affects Subcortical Structures, Resting-State Functional Connectivity, and Mental Health Problems in Early Adolescence.Brain sciences · 2023Article
- Public Opinions About Palliative and End-of-Life Care During the COVID-19 Pandemic: Twitter-Based Content Analysis.JMIR formative research · 2023Article
- Characterizing the Discourse of Popular Diets to Describe Information Dispersal and Identify Leading Voices, Interaction, and Themes of Mental Health: Social Network Analysis.JMIR infodemiology · 2023Article
- Machine learning of language use on Twitter reveals weak and non-specific predictions.NPJ digital medicine · 2022Article
- Understanding the Emotional Intelligence Discourse on Social Media: Insights from the Analysis of Twitter.Journal of Intelligence · 2021Article
- Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2021Review
- Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study.JMIR mental health · 2021Article
- Ethics and Privacy in Social Media Research for Mental Health.Current psychiatry reports · 2020Review
- Human-Computer Interaction, Ethics, and Biomedical Informatics.Yearbook of medical informatics · 2020Review
- Challenges and opportunities for public health made possible by advances in natural language processing.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2020Article
- Views on social media and its linkage to longitudinal data from two generations of a UK cohort study.Wellcome open research · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDepression is a common disorder that still remains underdiagnosed and undertreated in the UK National Health Service. Charities and voluntary organizations offer mental health services, but they are still struggling to promote these services to the individuals who need them. By analyzing social media (SM) content using machine learning techniques, it may be possible to identify which SM users are currently experiencing low mood, thus enabling the targeted advertising of mental health services to the individuals who would benefit from them.
objectiveThis study aimed to understand SM users' opinions of analysis of SM content for depression and targeted advertising on SM for mental health services.
methodsA Web-based, mixed methods, cross-sectional survey was administered to SM users aged 16 years or older within the United Kingdom. It asked participants about their demographics, their usage of SM, and their history of depression and presented structured and open-ended questions on views of SM content being analyzed for depression and views on receiving targeted advertising for mental health services.
resultsA total of 183 participants completed the survey, and 114 (62.3%) of them had previously experienced depression. Participants indicated that they posted less during low moods, and they believed that their SM content would not reflect their depression. They could see the possible benefits of identifying depression from SM content but did not believe that the risks to privacy outweighed these benefits. A majority of the participants would not provide consent for such analysis to be conducted on their data and considered it to be intrusive and exposing.
conclusionsIn a climate of distrust of SM platforms' usage of personal data, participants in this survey did not perceive that the benefits of targeting advertisements for mental health services to individuals analyzed as having depression would outweigh the risks to privacy. Future work in this area should proceed with caution and should engage stakeholders at all stages to maximize the transparency and trustworthiness of such research endeavors.
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Registered trials
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.