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Record W4410765448 · doi:10.2196/65887

Evaluating the Feasibility of Web-Monitoring Methodology for Measuring Exposure to Online Cancer Misinformation

2025· article· en· W4410765448 on OpenAlexvenueno aff
Cindy Turner, Ida Tovar, Rachel Codden, Jia–Wen Guo, Anne C. Kirchhoff, Margaret Raber, Deanna Kepka

Bibliographic record

VenueJMIR Cancer · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institutes of Health
KeywordsPreprintMisinformationComputer scienceWorld Wide WebComputer security

Abstract

fetched live from OpenAlex

Unlabelled: Understanding the impact of online cancer misinformation exposure on health outcomes is an area of growing concern, but few methods exist to objectively measure this exposure. The primary aim of this paper is to describe the lessons learned in using web-monitoring software to measure exposure to online cancer misinformation among patients with cancer. These lessons learned emerged from our experience conducting a prospective pilot study from October 2022 to August 2023 wherein we adopted commercially available web-monitoring software to capture cancer-related web content. A total of 56 patients with cancer completed a baseline survey, and 17 of these participants installed web-monitoring software on their personal computer for 30 days and completed a follow-up survey. We use implementation outcomes to describe the feasibility of this methodological approach using lessons learned in 3 topic areas, namely data quality, software implementation, and participant acceptability. We found the web-monitoring data to be appropriate for our research aim to objectively measure cancer misinformation exposure, although compatibility issues with social media websites and mobile devices negatively impacted data quality. A complex installation process negatively impacted implementation and caused an unknown number of participants to drop out after the baseline survey. Among participants who completed the study, reported acceptability of web-monitoring software for research purposes was high, though potentially biased by selective retention. This pilot study testing web-monitoring software for research purposes among patients with cancer demonstrates high acceptability but low feasibility due to implementation barriers. We propose practical solutions to address these barriers and believe the lessons learned here offer a promising foundation for improving methods to objectively measure patient exposure to online cancer information. Future studies should focus on exploring perceptions of web-monitoring among nonparticipants, considering alternative approaches, and expanding web-monitoring to include mobile devices.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.519
GPT teacher head0.589
Teacher spread0.070 · 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 designOther design
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

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