ArticleJournal of the American Medical Informatics Association : JAMIA2017
Using social media to monitor mental health discussions - evidence from Twitter.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Extracting Quality of Life Information of Patients Diagnosed With Breast Cancer From Health Care Online Forum Posts: Data Feasibility Study.JMIR cancer · 2026Article
- Identifying Dietary Supplements Related Effects from Social Media by ChatGPT.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study.Npj mental health research · 2024Article
- Wellness Influencer Responses to COVID-19 Vaccines on Social Media: A Longitudinal Observational Study.Journal of medical Internet research · 2024Observational
- Short-term changes in mental health help-seeking behaviors following exposure to multiple social stressors and a natural disaster.Social science & medicine (1982) · 2024Article
- An Evaluation of the Boys Do Cry Suicide Prevention Media Campaign on Twitter: Mixed Methods Approach.JMIR formative research · 2023Article
- Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study.Journal of medical Internet research · 2023Article
- Modeling information diffusion in social media: data-driven observations.Frontiers in big data · 2023Article
- Using Twitter Data to Estimate the Prevalence of Symptoms of Mental Disorders in the United States During the COVID-19 Pandemic: Ecological Cohort Study.JMIR formative research · 2022Article
- Synergy Between Public and Private Health Care Organizations During COVID-19 on Twitter: Sentiment and Engagement Analysis Using Forecasting Models.JMIR medical informatics · 2022Article
- Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis.JAMIA open · 2022Article
- Exploring Public Perceptions of Dental Care Affordability in the United States: Mixed Method Analysis via Twitter.JMIR formative research · 2022Article
- Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set.JMIR medical informatics · 2022Article
- Using social media listening and data mining to understand travellers' perspectives on travel disease risks and vaccine-related attitudes and behaviours.Journal of travel medicine · 2022Article
- Social Service Workers' Use of Social Media to Obtain Client Information: Current Practices and Perspectives on a Potential Informatics Platform.Journal of social service research · 2022Article
- The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States.Health data science · 2022Article
- Harnessing Twitter data to survey public attention and attitudes towards COVID-19 vaccines in the UK.Scientific reports · 2021Article
- Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak.International journal of medical informatics · 2021Article
- The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health.Information systems frontiers : a journal of research and innovation · 2021Article
- Indications of Depressive Symptoms During the COVID-19 Pandemic in Germany: Comparison of National Survey and Twitter Data.JMIR mental health · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesGiven the public health importance of communicating about mental illness and the growing use of social media to convey information, our goal was to develop an empirical model to identify periods of heightened interest in mental health topics on Twitter. MATERIALS AND
methodsWe collected data on 176 million tweets from 2011 to 2014 with content related to depression or suicide. Using an autoregressive integrated moving average (ARIMA) data analysis, we identified deviations from predicted trends in communication about depression and suicide.
resultsTwo types of heightened Twitter activity regarding depression or suicide were identified in 2014: expected increases in response to planned behavioral health events, and unexpected increases in response to unanticipated events. Tweet volume following expected increases went back to the predicted level more rapidly than the volume following unexpected events. DISCUSSION: Although ARIMA models have been used extensively in other fields, they have not been used widely in public health. Our findings indicate that our ARIMA model is valid for identifying periods of heightened activity on Twitter related to behavioral health. The model offers an objective and empirically based measure to identify periods of greater interest for timing the dissemination of credible information related to mental health.
conclusionSpikes in tweet volume following a behavioral health event often last for less than 2 days. Individuals and organizations that want to disseminate behavioral health messages on Twitter in response to heightened periods of interest need to take this limited time frame into account.
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