How coordinated link sharing behavior and partisans’ narrative framing fan the spread of COVID-19 misinformation and conspiracy theories
Bibliographic record
Abstract
This study examines the presence and role of Coordinated Link Sharing Behavior (CLSB) on Facebook around the "America's Frontline Doctors" press conference, and the promotion of several unproven conspiracy theories including the false assertion that hydroxychloroquine is a "cure" for COVID-19 by Dr. Stella Immanuel, one of the doctors who took part in the press conference. We collected 7,737 public Facebook posts mentioning Stella Immanuel using CrowdTangle and then applied the specialized program CooRnet to detect CLSB among Facebook public pages, groups and verified profiles. Finally, we used a mixed-method approach consisting of both network and content analysis to examine the nature and scope of the detected CLSB. Our analysis shows how Facebook accounts engaged in CLSB to fuel the spread of misinformation. We identified a coalition of Facebook accounts that engaged in CLSB to promote COVID-19 related misinformation. This coalition included US-based pro-Trump, QAnon, and anti-vaccination accounts. In addition, we identified Facebook accounts that engaged in CLSB in other countries, such as Brazil and France, that primarily promoted hydroxychloroquine, and some accounts in African countries that criticized the government's pandemic response in their countries.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".