Infographics as an effective method of scientific communication with social media users for COVID-19 topics: A survey study (Preprint)
Bibliographic record
Abstract
BACKGROUND Since the beginning of the COVID-19 pandemic, the world has also been battling a COVID-19 infodemic. Navigating for accurate information, especially health- and science-related content, on social media has been challenging. Although infographics are a popular medium for simplifying complex, text-based information into visual components, their usefulness in communicating COVID-19 related information during a global health crisis has not been explored. OBJECTIVE The study aims to explore the effectiveness of infographics in conveying scientific information related to COVID-19 on social media. METHODS Following a social media campaign that published COVID-19 related infographics, a cross-sectional survey was administered to social media users, primarily students from Western University. Descriptive statistics were used to summarize all Likert-type scale responses and an inductive qualitative analysis was performed for open ended responses. Differences in responses based on educational background (health vs non-health related) were analyzed using Fischer’s exact. RESULTS 361 survey responses were collected. 73% of respondents were young adults (18-24) with varying degrees of post-secondary education in a health-related academic background. Most respondents indicated that infographics were visually appealing and were likely to share infographics as reliable sources of information. The use of infographics as an effective tool for science communication was strongly supported. Compared to written articles, majority of the survey responders agreed that infographics allowed for greater information retention and learning. Educational background did not influence the perceived usefulness of infographics in understanding scientific information. CONCLUSIONS Infographics are effective in conveying scientific information about COVID-19 on social media. Findings from this study can be useful for shaping communication strategies during a pandemic and, more broadly, global crises.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".