ArticleHealth informatics journal2020
Assessing mental health signals among sexual and gender minorities using Twitter data.
Article in Health informatics journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed.
- Biases in using social media data for public health surveillance: A scoping review.International journal of medical informatics · 2022Article
- Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review.JMIR medical informatics · 2021Article
- Deep learning models in detection of dietary supplement adverse event signals from Twitter.JAMIA open · 2021Article
- Exploring Eating Disorder Topics on Twitter: Machine Learning Approach.JMIR medical informatics · 2020Article
- Mining Twitter to Assess the Determinants of Health Behavior towards Palliative Care in the United States.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities.Computer supported cooperative work : CSCW : an international journal · 2020Article
- A comparative analysis of anti-vax discourse on twitter before and after COVID-19 onset.Health informatics journalArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Sexual and gender minorities face extreme challenges that breed stigma with alarming consequences damaging their mental health. Nevertheless, sexual and gender minority people and their mental health needs remain little understood. Because of stigma, sexual and gender minorities are often unwilling to self-identify themselves as sexual and gender minorities when asked. However, social media have become popular platforms for health-related researches. We first explored methods to find sexual and gender minorities through their self-identifying tweets, and further classified them into 11 sexual and gender minority subgroups. We then analyzed mental health signals extracted from these sexual and gender minorities' Twitter timelines using a lexicon-based analysis method. We found that (1) sexual and gender minorities expressed more negative feelings, (2) the difference between sexual and gender minority and non-sexual and gender minority people is shrinking after 2015, (3) there are differences among sexual and gender minorities lived in different geographic regions, (4) sexual and gender minorities lived in states with sexual and gender minority-related protection laws and policies expressed more positive emotions, and (5) sexual and gender minorities expressed different levels of mental health signals across different sexual and gender minority subgroups.
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What OpenQuestion holds
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.