Sharing Reliable COVID-19 Information and Countering Misinformation: In-Depth Interviews With Information Advocates
Bibliographic record
Abstract
BACKGROUND: The rampant spread of misinformation about COVID-19 has been linked to a lower uptake of preventive behaviors such as vaccination. Some individuals, however, have been able to resist believing in COVID-19 misinformation. Further, some have acted as information advocates, spreading accurate information and combating misinformation about the pandemic. OBJECTIVE: This work explores highly knowledgeable information advocates' perspectives, behaviors, and information-related practices. METHODS: To identify participants for this study, we used outcomes of survey research of a national sample of 1498 adults to find individuals who scored a perfect or near-perfect score on COVID-19 knowledge questions and who also self-reported actively sharing or responding to news information within the past week. Among this subsample, we selected a diverse sample of 25 individuals to participate in a 1-time, phone-based, semistructured interview. Interviews were recorded and transcribed, and the team conducted an inductive thematic analysis. RESULTS: Participants reported trusting in science, data-driven sources, public health, medical experts, and organizations. They had mixed levels of trust in various social media sites to find reliable health information, noting distrust in particular sites such as Facebook (Meta Platforms) and more trust in specific accounts on Twitter (X Corp) and Reddit (Advance Publications). They reported relying on multiple sources of information to find facts instead of depending on their intuition and emotions to inform their perspectives about COVID-19. Participants determined the credibility of information by cross-referencing it, identifying information sources and their potential biases, clarifying information they were unclear about with health care providers, and using fact-checking sites to verify information. Most participants reported ignoring misinformation. Others, however, responded to misinformation by flagging, reporting, and responding to it on social media sites. Some described feeling more comfortable responding to misinformation in person than online. Participants' responses to misinformation posted on the internet depended on various factors, including their relationship to the individual posting the misinformation, their level of outrage in response to it, and how dangerous they perceived it could be if others acted on such information. CONCLUSIONS: This research illustrates how well-informed US adults assess the credibility of COVID-19 information, how they share it, and how they respond to misinformation. It illustrates web-based and offline information practices and describes how the role of interpersonal relationships contributes to their preferences for acting on such information. Implications of our findings could help inform future training in health information literacy, interpersonal information advocacy, and organizational information advocacy. It is critical to continue working to share reliable health information and debunk misinformation, particularly since this information informs health behaviors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".