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Record W4391623400 · doi:10.2196/54000

Using Social Listening for Digital Public Health Surveillance of Human Papillomavirus Vaccine Misinformation Online: Exploratory Study

2024· article· en· W4391623400 on OpenAlexvenueno aff
Dannell Boatman, Abby Starkey, Lori Acciavatti, Zachary Jarrett, Amy Allen, Stephenie Kennedy-Rea

Bibliographic record

VenueJMIR Infodemiology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsMisinformationActive listeningPublic healthSocial mediaPublic health surveillanceHealth communicationRepresentativeness heuristicComputer scienceInternet privacyExploratory researchPsychologyMedicineWorld Wide WebSocial psychologySociologyComputer securityCommunication

Abstract

fetched live from OpenAlex

Despite challenges related to the data quality, representativeness, and accuracy of artificial intelligence-driven tools, commercially available social listening platforms have many of the attributes needed to be used for digital public health surveillance of human papillomavirus vaccination misinformation in the online ecosystem.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.246
GPT teacher head0.482
Teacher spread0.237 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations8
Published2024
Admission routes1
Has abstractyes

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