The Virus Gone Viral: The October 4th Conspiracy, “X”, and Post-Truth
Bibliographic record
Abstract
On October 4th, 2023, the Federal Emergency Management Agency (FEMA) and Federal Communications Commission (FCC) conducted a nationwide test of the Emergency Alert System and Wireless Emergency Alerts, broadcasting a message to all consumer cell phones in the United States; This routine test became the catalyst for a baseless conspiracy theory involving 5G, wave frequencies, and zombies within the anti-vaccine community and gained significant traction online. In the context of a post-truth world, the proliferation of such dangerous misinformation warrants an examination of the role played by social media platforms, particularly "X" (formerly "Twitter"), in disseminating the October 4th Zombie conspiracy theory. This study explores how social media facilitates the dissemination and perpetuation of groundless theories devoid of objective truth within like-minded communities, and utilizes content analysis, discourse analysis, and an examination of user engagement with October 4th-related content on "X". What is found is that the rise of this conspiracy theory is largely attributed to the nature of the “X” algorithm: whether engaged with positively or negatively, engagement pushes content regardless of the nature of the post and therefore enables the widespread of the conspiracy theory across the platform to be viewed by millions. Thus, these findings bring forth the question of who is responsible for regulating informational versus misinformational discourse if we live in a post-truth era.
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 | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".