ArticleScience advances2023
AI model GPT-3 (dis)informs us better than humans.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Generative AI and health misinformation: production, propagation, and mitigation-a systematic review.BMC public health · 2026Pooled it
- On the conversational persuasiveness of GPT-4.Nature human behaviour · 2025Trial
- The 42 problem in AI-assisted research.eLife · 2026Article
- Development of an Equity-Centered Sociotechnical Architecture for Generative AI Integration in Public Health Promotion: Conceptual Framework.Journal of medical Internet research · 2026Article
- Can AI help reduce prejudice? Evaluating the effectiveness of AI-powered personalized persuasion on support for transgender rights.PNAS nexus · 2026Article
- Biased AI writing assistants shift users' attitudes on societal issues.Science advances · 2026Article
- How latent and prompting biases in AI-generated historical narratives influence opinions.PNAS nexus · 2026Article
- Beliefs and sharing intentions of human- and AI-generated fake news: Evidence from 27 European countries.PNAS nexus · 2026Article
- Large language models show Dunning-Kruger-like effects in multilingual fact-checking.Scientific reports · 2026Article
- Diverging trajectories of trust in healthcare and on-line information seeking: what's next with LLMs.NPJ digital medicine · 2026Article
- Large language models for surgical informed consent: an ethical perspective on simulated empathy.Journal of medical ethics · 2026Article
- How topic content shapes LLM personality-tailored persuasion: semantic anchoring and topic stereotype effects.Frontiers in psychiatry · 2026Article
- A meta-analysis of the persuasive power of large language models.Scientific reports · 2025Article
- The impact of advanced AI systems on democracy.Nature human behaviour · 2025Review
- Source framing triggers systematic bias in large language models.Science advances · 2025Article
- The perils of politeness: how large language models may amplify medical misinformation.NPJ digital medicine · 2025Article
- A large-scale dataset of AI-related tweets: Structure and descriptive statistics.Data in brief · 2025Article
- Comparison of Large Language Model with Aphasia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Countering AI-generated misinformation with pre-emptive source discreditation and debunking.Royal Society open science · 2025Article
- Labeling AI-generated media online.PNAS nexus · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is changing the way we create and evaluate information, and this is happening during an infodemic, which has been having marked effects on global health. Here, we evaluate whether recruited individuals can distinguish disinformation from accurate information, structured in the form of tweets, and determine whether a tweet is organic or synthetic, i.e., whether it has been written by a Twitter user or by the AI model GPT-3. The results of our preregistered study, including 697 participants, show that GPT-3 is a double-edge sword: In comparison with humans, it can produce accurate information that is easier to understand, but it can also produce more compelling disinformation. We also show that humans cannot distinguish between tweets generated by GPT-3 and written by real Twitter users. Starting from our results, we reflect on the dangers of AI for disinformation and on how information campaigns can be improved to benefit global health.
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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.