Article2018 IEEE International Conference on Healthcare Informatics Workshops : proceedings : 4-7 June 2018, New York, NY. International Conference on Healthcare Informatics (6th : 2018 : New York, N.Y.). Workshops2018
Assessing Mental Health Signals among Sexual and Gender Minorities using Twitter Data.
Article in 2018 IEEE International Conference on Healthcare Informatics Workshops : proceedings : 4-7 June 2018, New York, NY. International Conference on Healthcare Informatics (6th : 2018 : New York, N.Y.). Workshops, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Mental health inequities affecting sexual and gender diverse individuals during the early COVID-19 period in Massachusetts.PLOS mental health · 2025Article
- Evaluation of clustering and topic modeling methods over health-related tweets and emails.Artificial intelligence in medicine · 2021Article
- Investigating Public Discourses Around Gender and COVID-19: a Social Media Analysis of Twitter Data.Journal of healthcare informatics research · 2021Article
- Assessing mental health signals among sexual and gender minorities using Twitter data.Health informatics journal · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Sexual and gender minorities' (SGMs) mental health needs remain little understood. Because of stigma and discrimination, SGMs are often unwilling to self-identify and reluctant to participate in traditional surveys. On the other hand, social media platforms have brought rapid changes to the health communication landscape and provided us a new data source for health surveillance of vulnerable populations. In this study, we explored machine learning methods to identify SGM individuals through finding their self-identifying tweets; then, applied a lexicon-based text analysis method to extract emotion and mental health signals from SGMs' Twitter timelines. We found that 1) SGM people have expressed more negative feelings in their tweets, and 2) within SGM populations, gay and genderfluid individuals tend to use more words related to negative emotions, anger, anxiety, and sadness in their tweets.
Indexed as
Identifiers
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.