The Instagram Infodemic Persists: Extreme Content Escapes Platform's Removal Tactics
Bibliographic record
Abstract
The general cobranding of conspiracy theories and COVID-19 misinformation has been shared at an alarming rate on social media platforms. Instagram has attempted an initiative to flag and/or remove health misinformation and/or disinformation; however, the efficacy of these efforts has been unclear. This study aimed to re-examine 300 posts collected in a previous study evaluating trends in misinformation removal process on Instagram. One hundred eighty-three of 300 original posts remained on the platform, most of which were from the hashtag #hoax. Only one post was flagged for containing false information, despite presence in more than one post. The claims that the platform is removing or flagging misinformation does not align with these findings and amplifies the concern for public safety for Instagram users. Sharing and removal patterns among the 300 posts suggest that conspiracy theorists or those exposed to the inaccurate information may be at higher risk of believing and propagating other unsupported theories.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".