Longitudinal Analysis of Health Misinformation: A Case of COVID-19 Pandemic
Bibliographic record
Abstract
We conduct a longitudinal analysis of health misinformation to understand its temporal patterns and impact on public health behaviors. Informed by the Health Belief Model, we analyzed 12521 fact-checked claims related to COVID-19 posted between Jan 03, 2020, when the pandemic emerged, and May 11, 2023, when the public health emergency ended. Keeping up with the computational theory construction paradigm, we employed several methods to identify health belief topics, claim veracity (i.e., true, false, or misleading), media of origination (i.e., social or web media), and modality (i.e., lean or rich). Following the temporal bracketing approach, we analyze the patterns of misinformation (i.e., false and misleading claims) during eight stages encompassing several surge and recovery periods, as well as emergence and wind-down. Furthermore, we correlated misinformation patterns with vaccination trends to determine the impact of misinformation on public health behavior. Based on the empirical evidence, we develop propositions to theorize about the phenomenon of misinformation and health behavior. This study contributes by conducting a longitudinal analysis of health misinformation and provides insights into the patterns of health beliefs that originate on social and web media in different modalities. Further, this study contributes by providing insights into the impact of misinformation on health behaviors. The insights might interest several public health experts and policymakers in designing better communication and intervention strategies to counter the false narrative about the pandemic. The study could also inform the development of better approaches to identifying, monitoring, and fact-checking misinformation. Finally, the study lays the ground to examine further motivations, mechanisms, and impacts of sharing health misinformation on online platforms.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".