Evidence map›Paper›PMID 40854227›Full record

ArticleJournal of medical Internet research2025

Navigating Visibility on Weibo Among People Living With HIV: Qualitative Study.

Leixiao Zeng, Yunze Zhao, Wai-Kit Ming

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 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. 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

3 authors.

Leixiao ZengSchool of Journalism and Communication, Renmin University of China, Beijing, China.ORCID http://orcid.org/0009-0002-7843-6130
Yunze ZhaoSchool of Journalism and Communication, Renmin University of China, Beijing, China.ORCID http://orcid.org/0000-0002-5169-4765
Wai-Kit MingDepartment of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, To Yuen Building, 31 To Yuen Street, Hong Kong, China (Hong Kong), 852 34426956.ORCID http://orcid.org/0000-0002-8846-7515

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: By the end of 2022, 1.223 million people were living with HIV in China. Beyond medical challenges, they often face stigma and social exclusion. In China, Sina Weibo (Sina Corporation), with over 582 million monthly active users as of 2022, has emerged as a critical space for people living with HIV, many of whom identify as "A-friends." They navigated these complex dynamics of visibility. In this context, visibility, understood as both the capacity to be seen and the power relations it entails, is a central affordance of social media. Objective: This study aimed to explore how A-friends navigate their visibility on Weibo, focusing on the dual-edged nature of visibility. It examines how visibility can empower marginalized groups while also exposing them to risks. The study highlights the tension between these dynamics and aims to inform the creation of supportive digital environments that balance empowerment with protection from harm. Methods: We conducted nonparticipant observation and semistructured interviews with 30 A-friends, recruited through opportunistic and snowball sampling on social media platforms. The data were analyzed thematically using NVivo 11.0 (QSR International). Among the participants, 86.67% (26/30) were interviewed via internet-based voice chat, 10% (3/30) offline, and 3.33% (1/30) by text. To confirm theoretical saturation, 3 additional interviews were coded separately, yielding no new themes. Results: As shown by the data, the majority of participants (56.67%, 17/30) were aged between 30 years and 40 years, with 43.33% (13/30) holding a bachelor's degree or higher. Most participants (46.67%, 14/30) were diagnosed with HIV 1-5 years ago, and all participants were asymptomatic. After coding the interviews, we identified 2 overarching themes during the development of the coding framework, each comprising 3 subcategories, resulting in a total of 6 subcategories. Theme 1 highlighted the positive implications of visibility, referred to as the Climb Effect, which included (1) self-reconstruction through illness narratives, (2) relational bonding and community building, and (3) public advocacy to challenge stigma. Theme 2 focused on the negative consequences, termed the Slide Effect, which encompassed (1) the reproduction of social exclusion and limited public empathy, (2) privacy concerns and risks of unintended disclosure, and (3) ego depletion. Conclusions: This study highlights the layered and cyclical nature of visibility in online health communities, which we conceptualize through a visibility ladder model. Self-visibility promotes personal growth, health self-management, and psychological resilience but also introduces risks of self-stigmatization and emotional exhaustion. Social visibility strengthens peer support and shared identity while exposing individuals to privacy breaches and misinformation. Public visibility empowers collective action and advocacy, yet is constrained by persistent societal stigma and platform algorithms that limit audience reach. Future efforts should prioritize enhancing eHealth literacy, strengthening privacy protections, and promoting inclusive, stigma-reducing digital environments to optimize the benefits of visibility and mitigate its potential harms.

Indexed as

FriendsHIV InfectionsSocial MediaAdultChinaFemaleHumansMaleMiddle AgedQualitative ResearchSocial Stigmaempowermentinequalityonline health communitiessocial mediastigmavisibility

Identifiers

PMID40854227
PMCPMC12377793

What OpenQuestion holds

Textmetadata
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.