Evidence map›Paper›PMID 31120498›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2019

Mapping gender transition sentiment patterns via social media data: toward decreasing transgender mental health disparities.

Oliver L Haimson

Open access · hybridAbstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
6.5field-weighted citation impact, top 4% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.

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  17. Health informatics and health equity: improving our reach and impact.Journal of the American Medical Informatics Association : JAMIA · 2019
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Oliver L HaimsonSchool of Information, University of Michigan, Ann Arbor, Michigan, USA.
University of Michigan–Ann Arbor · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTransgender people face substantial mental health disparities, and this population's emotional well-being can be particularly volatile during gender transition. Understanding gender transition sentiment patterns can positively impact transgender people by enabling them to anticipate, and put support in place for, particularly difficult time periods. Yet, tracking sentiment over time throughout gender transition is challenging using traditional research methods. This study's objective was to use social media data to understand average gender transition sentiment patterns. MATERIALS AND

methodsComputational sentiment analysis and statistics were used to analyze 41 066 posts from 240 Tumblr transition blogs (online spaces where transgender people document gender transitions) to understand sentiment patterns over time and quantify relationships between transgender identity disclosures, sentiment, and social support.

resultsFindings suggest that sentiment increases over time on average throughout gender transition, particularly when people receive supportive responses to transgender identity disclosures. However, after disclosures to family members, people experienced temporary increased negative sentiment, followed by increased positive sentiment in the long term. After transgender identity disclosures on Facebook, an important means of mass disclosure, those with supportive networks experienced increased positive sentiment.

conclusionsWith foreknowledge of sentiment patterns likely to occur during gender transition, transgender people and their mental healthcare professionals can prepare with proper support in place throughout the gender transition process. Social media are a novel data source for understanding transgender people's sentiment patterns, which can help reduce mental health disparities for this marginalized population during a particularly difficult time.

Indexed as

Mental HealthSelf DisclosureSocial MediaSocial SupportBloggingFemaleHealth Status DisparitiesHumansMachine LearningMaleTransgender Personshealth status disparitiesmental healthminority healthsocial mediatransgender persons

Identifiers

PMID31120498
PMCPMC6696505
OpenAlexW2946800637

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.