MétaCan
Menu
Back to cohort
Record W4415598677 · doi:10.2196/85379

Mpox on Instagram: A content analytic study (Preprint)

2025· article· en· W4415598677 on OpenAlexvenueno aff
Elizabeth E. Havron, Kylee Chenault, Danny Valdez, Rebecca Houghton, Eric R. Buhi

Bibliographic record

VenueJMIR Infodemiology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsnot available
Fundersnot available
KeywordsInter-rater reliabilityGovernment (linguistics)Content analysisDescriptive statisticsSample (material)Public health

Abstract

fetched live from OpenAlex

<sec> <title>BACKGROUND</title> Mpox was declared a public health emergency of international concern in 2022. Instagram is widely used by age groups and communities disproportionately affected by mpox, yet platform-specific evidence on mpox information characteristics and engagement is limited. </sec> <sec> <title>OBJECTIVE</title> To characterize sources, content, and engagement features of mpox-related Instagram posts, to describe prevention and treatment framing, and to compare the top 10% most-liked posts with the remaining corpus. </sec> <sec> <title>METHODS</title> We retrieved English-language public Instagram posts via CrowdTangle containing “mpox” or “monkeypox” dated May 5, 2022 to January 17, 2023 (initial N=18,616). Using a pretested, deductive codebook adapted from prior Instagram health studies, two coders completed two pilot rounds; variables with low agreement were excluded. Interrater reliability across retained variables showed mean κ=0.70 (median κ=0.83). A randomized analytic sample of N=1,000 posts was coded for source type, content features, and prevention/treatment framing. Descriptive statistics were computed. For engagement contrasts, we compared the top 10% most-liked posts with the bottom 90% using tests of differences in independent proportions (mean differences [MD] with p-values). </sec> <sec> <title>RESULTS</title> Most posts originated from organizations (76.0%) versus individuals (24.0%). Organizational sources most commonly included businesses (57.4%) and news/media outlets (52.8%); government (22.9%), nonprofits (17.3%), and health-care organizations (9.2%) were less frequent. About one-third of posts cited a source (34.4%), most often the WHO and CDC/other federal entity. Posts predominantly used illustrated images/infographics (82.7%); photos appeared in 47.3% and videos in 12.4% of posts. Prevention content appeared in 38.4% of posts, most commonly vaccination (68.5% of prevention posts), followed by avoiding close contact (14.5%), avoiding contact with objects (8.3%), abstaining from sexual activity (7.6%), and condom use (1.3%); 28.9% of prevention posts noted barriers. Treatment mentions were uncommon (2.5% traditional biomedical; 0.2% alternative). Compared with the bottom 90%, the top 10% most-liked posts: 1) were more likely to originate from public figures/celebrities among individuals (MD=-0.591; p&lt;.001) and from businesses (MD=-0.299; p&lt;.001) or news/media (MD=-0.350; p&lt;.001) among organizations; 2) were less likely to be from government, nonprofit, or health-care organizations (all p&lt;.01); and more often included non-moving images (MD=-0.119; p&lt;.05), visible lesion depictions (MD=-0.081; p&lt;.05), prevalence mentions (MD=-0.180; p&lt;.001), and citations (MD=-0.162; p&lt;.01). </sec> <sec> <title>CONCLUSIONS</title> During the initial outbreak period, highly engaged mpox content on Instagram skewed toward posts by public figures and news/business accounts and toward static, citation-bearing visuals that included prevalence context and occasionally lesion imagery. Public-health communicators seeking reach on Instagram should prioritize clear static infographics with explicit source citation and epidemiologic context and consider co-publishing with trusted creators and news outlets, while addressing access barriers highlighted in prevention posts. </sec>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.359
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJMIR InfodemiologySame topicPoxvirus research and outbreaksFrench-language works237,207