ArticleNature communications2024
Human detection of political speech deepfakes across transcripts, audio, and video.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- How deceptive online networks reached millions in the US 2020 elections.Nature human behaviour · 2026Article
- Article
- The continued influence of AI-generated deepfake videos despite transparency warnings.Communications psychology · 2026Article
- AI-generated images of familiar faces are indistinguishable from real photographs.Cognitive research: principles and implications · 2025Article
- Generative propaganda: Evidence of AI's impact from a state-backed disinformation campaign.PNAS nexus · 2025Article
- People are poorly equipped to detect AI-powered voice clones.Scientific reports · 2025Article
- Human detection of political speech deepfakes across transcripts, audio, and video.Nature communications · 2024Article
- Cortical-striatal brain network distinguishes deepfake from real speaker identity.Communications biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in technology for hyper-realistic visual and audio effects provoke the concern that deepfake videos of political speeches will soon be indistinguishable from authentic video. We conduct 5 pre-registered randomized experiments with N = 2215 participants to evaluate how accurately humans distinguish real political speeches from fabrications across base rates of misinformation, audio sources, question framings with and without priming, and media modalities. We do not find base rates of misinformation have statistically significant effects on discernment. We find deepfakes with audio produced by the state-of-the-art text-to-speech algorithms are harder to discern than the same deepfakes with voice actor audio. Moreover across all experiments and question framings, we find audio and visual information enables more accurate discernment than text alone: human discernment relies more on how something is said, the audio-visual cues, than what is said, the speech content.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.