Artificial Intelligence and Political Deepfakes: Shaping Citizen Perceptions Through Misinformation
Bibliographic record
Abstract
In the post-truth age, political conspiracies circulate rapidly on social media, cultivating false narratives, while challenging the public’s ability to distinguish truth from fiction. ‘Deepfakes’ represent the most recent type of misinformation. They display deceitful representations of events to lead audiences to believe in fabricated realities. There has been limited research on deepfakes in political communications. As this technology progresses, deepfakes look deceptively authentic; thus, it is necessary to explore their effects on public perceptions. This study examines viewers’ comments on an Instagram-published deepfake video of Hillary Clinton to understand the impact of this technology. The results demonstrate that individuals struggle to identify deepfake videos and that their opinions are affected by this persuasive type of misinformation. This study also explores different ethical concerns posed by political deepfakes. By offering insights into public reactions to manipulated content, this study contributes to our understanding of the political effects of AI-fabricated content.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".