The Instagram Infodemic: Cobranding of Conspiracy Theories, Coronavirus Disease 2019 and Authority-Questioning Beliefs
Bibliographic record
Abstract
The novel coronavirus 2019 pandemic has brought about an overabundance of misinformation concerning the virus (SARS-CoV-2) and the coronavirus disease 2019 (COVID-19) it causes spreading rapidly on social media. While some more obviously untrustworthy sources may be easier for social media filters to identify and remove, an early feature was the cobranding of COVID-19 misinformation with other types of misinformation. To examine this, the top 10 Instagram posts (in English) were collected every day for 10 days (April 21-30th, 2020) for each of the hashtags #hoax, #governmentlies, and #plandemic. The #hoax was selected first as it is commonly used in conspiracy theory posts, and #governmentlies because it was the most commonly cotagged with #hoax. For comparison, we selected #plandemic as the most popular cotagged hashtag that was clearly COVID-19-related. This resulted in 300 Instagram posts available for our analysis. We conducted a content analysis by coding the themes contained in the posts, both for the images and the text caption shared by the Instagram users (including hashtags). The broad theme of general mistrust was the most common, including the idea that the government and/or media has fabricated or hidden information pertaining to COVID-19. Conspiracy theories were the second-most frequent theme among posts. Overall, COVID-19 was frequently presented in association with authority-questioning beliefs. Developing an understanding of how the public shares misinformation on COVID-19 alongside conspiracy theories and authority-questioning statements can aid public health officials and policymakers in limiting the spread of potentially life-threatening health misinformation.
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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 | 0.002 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".