Evidence map›Paper›PMID 40921069›Full record

ArticleJournal of medical Internet research2025

Digital Hostility Toward LGBTQIA+ Research Recruitment on Social Media Using Topic Modeling and Sentiment Analysis of Facebook Comments: Quantitative Content Analysis Study.

Violeta J Rodriguez, Brett Peterson, Ashley Benhayoun, Qimin Liu

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Violeta J RodriguezClinical Community Division, Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, United States.ORCID https://orcid.org/0000-0001-8543-2061
Brett PetersonDepartment of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, United States.ORCID https://orcid.org/0009-0008-3583-5530
Ashley BenhayounDepartment of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, United States.ORCID https://orcid.org/0000-0002-8328-3506
Qimin LiuDepartment of Psychological and Brain Sciences, Boston University, Boston, United States.ORCID https://orcid.org/0000-0003-3840-1136

Funding

Optimizing the Assessment of Parenting: A Multi-Method and Multi-Informant ApproachDP5OD036508 · OD · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI Violeta de Jesus Rodriguez · 2024 to 2026
$1.2M
NIH HHS DP5 OD036508
6 · The paper itself

Abstract

backgroundLesbian, gay, bisexual, transgender, queer/questioning, intersex, asexual (LGBTQIA+) researchers and participants frequently encounter hostility in virtual environments, particularly on social media platforms where public commentary on research advertisements can foster stigmatization. Despite a growing body of work on researcher virtual hostility, little empirical research has examined the actual content and emotional tone of public responses to LGBTQIA+-focused research recruitment.

objectiveThis study aimed to analyze the thematic patterns and sentiment of social media comments directed at LGBTQIA+ research recruitment advertisements, in order to better understand how virtual stigma is communicated and how it may impact both researchers and potential participants.

methodsA total of 994 publicly visible Facebook comments posted in response to LGBTQIA+ recruitment advertisements (January to April 2024) were collected and analyzed. Text preprocessing included tokenization, stop-word removal, and lemmatization. Latent Dirichlet allocation was used to identify latent themes across the dataset. Sentiment analysis was conducted using the Bing Liu and National Research Council lexicons, with scores ranging from -1 (most negative) to 1 (most positive). Linguistic Inquiry and Word Count was used to quantify psychological and moral language features. Comments were also manually coded into four audience target groups (researchers, LGBTQIA+ community, general public, and other commenters), and language category differences were analyzed using 1-way ANOVAs with Bonferroni corrections.

resultsTopic modeling identified three key themes: (1) "Transitions, Health, and Gender Dysphoria," (2) "Polarized Debate and Response," and (3) "Religious and Ideological Debates." Topic 2 had the highest average prevalence (average γ=0.486, SD 0.21). Sentiment analysis revealed negative mean sentiment scores for all three topics: Topic 1 (-0.41, SD 0.48), Topic 2 (-0.21, SD 0.44), and Topic 3 (-0.35, SD 0.46). No topic exhibited a statistically significant predominance of positive sentiment. A 1-way ANOVA showed significant differences in linguistic tone across target groups: negative tone (F

conclusionsThis study documents a persistently hostile virtual environment for LGBTQIA+ research, where researchers are frequently dehumanized and LGBTQIA+ identities are pathologized. These findings reinforce stigma communication models and suggest a need for institutional responses that include mental health support, enhanced moderation tools, and policy advocacy. Future research should investigate how hostile discourse affects researchers' well-being and recruitment outcomes, and evaluate interventions to foster more respectful engagement with LGBTQIA+ studies.

Indexed as

HostilitySexual and Gender MinoritiesSocial MediaFemaleHumansMaleSocial StigmaFacebook commentsLGBTQIA+ researchLinguistic Inquiry and Word CountLIWCresearcher well-beingsentiment analysisstigma communicationtext miningtopic modelingvirtual stigma

Identifiers

PMID40921069
PMCPMC12455171

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.