Evidence map›Paper›PMID 38959061›Full record

ArticleJMIR mental health2024

News Media Framing of Suicide Circumstances and Gender: Mixed Methods Analysis.

Jasmine C Foriest, Shravika Mittal, Eugenia Kim, Andrea Carmichael, Natalie Lennon, Steven A Sumner, Munmun De Choudhury

Abstract read
In one paragraph

Article in JMIR mental health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

2 citing papers in PubMed.

  1. Article
  2. Review
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

7 authors.

Jasmine C ForiestSchool of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.ORCID 0000-0002-5265-9574
Shravika MittalSchool of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.ORCID 0000-0002-4888-7996
Eugenia KimSchool of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.ORCID 0009-0003-9619-0768
Andrea CarmichaelNational Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States.ORCID 0000-0002-0821-4471
Natalie LennonNational Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States.ORCID 0000-0002-4749-092X
Steven A SumnerNational Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States.ORCID 0000-0002-3805-7235
Munmun De ChoudhurySchool of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.ORCID 0000-0002-8939-264X

Funding

Leveraging Social Media Data and Machine Learning to Optimize Treatment Paradigms for Youth with SchizophreniaR01MH117172 · NIMH · GEORGIA INSTITUTE OF TECHNOLOGY · PI DE CHOUDHURY, MUNMUN · 2019 to 2022
$2.6M
NIMH NIH HHS R01 MH117172
6 · The paper itself

Abstract

backgroundSuicide is a leading cause of death worldwide. Journalistic reporting guidelines were created to curb the impact of unsafe reporting; however, how suicide is framed in news reports may differ by important characteristics such as the circumstances and the decedent's gender.

objectiveThis study aimed to examine the degree to which news media reports of suicides are framed using stigmatized or glorified language and differences in such framing by gender and circumstance of suicide.

methodsWe analyzed 200 news articles regarding suicides and applied the validated Stigma of Suicide Scale to identify stigmatized and glorified language. We assessed linguistic similarity with 2 widely used metrics, cosine similarity and mutual information scores, using a machine learning-based large language model.

resultsNews reports of male suicides were framed more similarly to stigmatizing (P<.001) and glorifying (P=.005) language than reports of female suicides. Considering the circumstances of suicide, mutual information scores indicated that differences in the use of stigmatizing or glorifying language by gender were most pronounced for articles attributing legal (0.155), relationship (0.268), or mental health problems (0.251) as the cause.

conclusionsLinguistic differences, by gender, in stigmatizing or glorifying language when reporting suicide may exacerbate suicide disparities.

Indexed as

Mass MediaSocial StigmaSuicideAdultFemaleHumansMaleSex Factorsdigital mental healthdisparitiesframinggenderglorificationglorifyglorifyingjournalismjournalistjournalistslanguage modellanguage modelslinguisticlinguisticsLLMLLMsmachine learningmediamHealthnatural language processingnewsNLPreporterreportersreportingreporting guidelinesself harmstigmastigmatizationstigmatizingsuicidalsuicidesuicides

Identifiers

PMID38959061
PMCPMC11255531

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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.