Disinformation claims and public opinion: evidence from a survey experiment in Georgia
Bibliographic record
Abstract
The spread of foreign disinformation is widely believed to constitute a threat to democracy. Yet when the notion of disinformation is salient, partisan actors may strategically invoke disinformation to raise doubts about politically damaging information. This analysis investigates disinformation claims as a political tactic – as a means of deflecting responsibility. We conducted a survey experiment (n = 1200) on a nationally representative sample in Georgia, which has been targeted by Russian disinformation. Respondents are shown a vignette accusing a presidential candidate of corruption and are randomly assigned one of four denials relating to disinformation. We find that disinformation defenses, even ones implicating Russia, do not reduce perceptions of culpability. Respondents spurn the fictitious candidate regardless of his deflections and party affiliation. We conclude that domestic disaffection with politics rendered the candidate's political excuses ineffective. These results contribute to the literature on mis/disinformation, political scandals, and blame avoidance. They suggest that disinformation salience is not sufficient to make disinformation defenses compelling. Furthermore, externalizing blame can backfire if a scandal appears plausible. Ironically, societal resistance to political excuses may hinder politicians’ efforts to deceive the public, but it also makes it easier for Russia (or others) to successfully execute actual disinformation campaigns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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".