Bibliographic record
Abstract
Viral online disinformation is misleading content that is generated to manipulate public opinion and to circulate rapidly in the digital space. Although viral disinformation has become an instrument for radicalization, the specific psychological mechanisms by which disinformation can be weaponized––wielded as mobilizing and radicalizing political tools––are not yet well-understood. In this paper, we establish the potential of concerted disinformation efforts to impact mass radicalization and political violence, first through historical precedents of deadly disinformation campaigns, then in modern-day examples from the USA and Russia. Comparing and contrasting political effects of two recent disinformation campaigns, QAnon’s #SaveTheChildren campaign in the USA, and anti-LGBTQ disinformation campaign in Russia, this paper highlights the significance of LGBTQ contagion threat—a notion that people can be “turned” into LGBTQ through deliberate outside influence. The psychological and political consequences of such messaging, its main target audience, and vulnerability factors rendering individuals especially susceptible to its radicalizing effects are discussed.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".