Evidence map›Paper›PMID 40354646›Full record

ArticleJournal of medical Internet research2025

Stigma Attitudes Toward HIV/AIDS From 2011 Through 2023 in Japan: Retrospective Study in Japan.

Yi Piao, Nao Taguchi, Keisuke Harada, Kunihiro Hirahara, Yosuke Takaku, John Austin, KuanYeh Lee, Yui Shiozawa, Yunfei Cheng, Yoji Inoue

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

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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Yi PiaoGilead Sciences KK, Tokyo, Japan.ORCID https://orcid.org/0009-0008-3162-543X
Nao TaguchiGilead Sciences KK, Tokyo, Japan.ORCID https://orcid.org/0009-0009-1213-0949
Keisuke HaradaGilead Sciences KK, Tokyo, Japan.ORCID https://orcid.org/0009-0004-6948-2834
Kunihiro HiraharaGilead Sciences KK, Tokyo, Japan.ORCID https://orcid.org/0009-0003-2319-9251
Yosuke TakakuJapanese Network of People Living with HIV/AIDS, Tokyo, Japan.ORCID https://orcid.org/0009-0005-6512-4607
John AustinGilead Sciences (United States), Foster City, CA, United States.ORCID https://orcid.org/0009-0009-3652-4096
KuanYeh LeeGilead Sciences KK, Tokyo, Japan.ORCID https://orcid.org/0009-0009-4879-8756
Yui ShiozawaDeloitte Tohmatsu Consulting LLC, Tokyo, Japan.ORCID https://orcid.org/0009-0006-5621-4421
Yunfei ChengDeloitte Tohmatsu Consulting LLC, Tokyo, Japan.ORCID https://orcid.org/0009-0002-1712-6470
Yoji InoueAccelight Inc, Tokyo, Japan.ORCID https://orcid.org/0009-0001-9105-7226

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStigma associated with HIV/AIDS continues to be a major barrier to prevention, management, and care. HIV stigma can negatively influence health behaviors. Surveys of the general public in Japan also demonstrated substantial gaps in knowledge of HIV/AIDS. Tweets from the social networking service X (formerly known as Twitter) have been studied to identify stigmas in other disorders but have not yet been used to study HIV stigma in Japan.

objectiveThe aim of the study is to examine the variations in stigma related to HIV over an extended period using tweets from X and to investigate the stigma toward people with HIV associated with various demographic segments.

methodsJapanese tweets from X related to HIV/AIDS were retrospectively collected; the phase 1 feasibility study collected tweets from 2011, 2014, and 2017, and the phase 2 analysis included tweets from each third year from 2011 through 2023. Individual tweets were labeled with the messages they conveyed (stigma and corresponding antistigma types included labels, marks, responsibility, peril, insults, and fear; tweets without stigma or antistigma messages were considered general education or neutral) along with demographic characteristics and locations; phase 1 results were used to develop a machine learning model to apply in phase 2. The labeled data from phase 2 were used to answer research questions concerning yearly changes in HIV stigma and proportions of stigma across population segments.

resultsA total of 2,016,826 tweets related to HIV/AIDS were identified over the study period; 1,648,556 (81.7%) were from individual accounts, with the remainder from organizational accounts. In total, 574,687 (28.5%) tweets indicated stigma attitudes, while 1,119,852 (55.5%), 207,320 (10.3%), and 114,967 (5.7%) showed neutral, antistigma, or general education attitudes, respectively. Tweets including peril, fear, or insult comprised 502,134 (87.4%) of tweets with stigma. The greatest numbers of tweets were made by people in their 20s, whereas people in their 20s and 60s had the greatest proportions of tweets with stigma (n=9650, 35.3% and n=558, 34.5%, respectively). Peril and fear made up 5819 (60.3%) of stigma tweets from people in their 20s. The proportion of tweets with stigma (n=59,719, 20.5% in 2017) increased notably during the COVID-19 pandemic (n=217,512, 31.4% in 2020, and a similar n=175,647, 33.9% in 2023). Tweets from health care practitioners had 1.68 times the odds of having antistigma messages versus those from others.

conclusionsThis study contributes to the understanding of HIV stigma in Japan and shows the usefulness of social media for studying stigma. The extent and type of HIV stigma changed from before to after the COVID-19 pandemic. These results can be used to develop future activities and educational programs to combat HIV-related stigma.

Indexed as

Acquired Immunodeficiency SyndromeHIV InfectionsSocial StigmaAdolescentAdultFemaleHumansJapanMaleMiddle AgedRetrospective StudiesSocial MediaYoung AdultHIVmachine learningsocial mediastigmaTwitterX

Identifiers

PMID40354646
PMCPMC12107205

What OpenQuestion holds

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LicenceCC BY
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.