COVID-19 Information Dissemination Via Social Media: Content Analysis of Instagram Posts During the COVID-19 Outbreak
Bibliographic record
Abstract
Introduction: Social media platforms provide easy access to an unprecedented volume of information which could influence the awareness and perception of people during public health crises. The current study aims to explore the trends and content of the posts on Instagram.Material and Methods: We performed a retrospective content analysis of available public messages posted on Instagram. We collected data between 23 January 2020 and 25 March 2020. The inclusion criteria included an Instagram post with a hashtag related to Coronavirus (i.e. # “Corona” and # “Coronavirus”, in the Persian language). Persian hashtags were used for retrieving posts. All posts were categorized into seven categories. We performed descriptive statistics with Microsoft Excel 2019 and SPSS version 26.Results: A total of 4280 posts were extracted, out of which 1281 were categorized into seven main categories including News (n=205, 26.7%), Criticism (n=136, 17.7%), Education (n=112, 14.6%), Coronavirus’s impact on the healthcare system (n=100, 13%), Combating Coronavirus (n=98, 12.8%), Coronavirus’s impact on society (n=89, 11.6%), Joke (n=28, 3.6%).Conclusion:Our findings revealed that the trend of posts on social media was influenced by factors such as the nature of the information sources as well as social and political occasions. This study provides insight into health dissemination on social media for future responses to public health 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".