mlsanityData to 7 Oct 2026

Comparing the willingness to share for human-generated vs. AI-generated fake news

arXiv 2402.07395 ↗First posted 12 Feb 2024cs.SI physics.soc-phcited by 12

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Abstract (arXiv)

Generative artificial intelligence (AI) presents large risks for society when it is used to create fake news. A crucial factor for fake news to go viral on social media is that users share such content. Here, we aim to shed light on the sharing behavior of users across human-generated vs. AI-generated fake news. Specifically, we study: (1) What is the perceived veracity of human-generated fake news vs. AI-generated fake news? (2) What is the user's willingness to share human-generated fake news vs. AI-generated fake news on social media? (3) What socio-economic characteristics let users fall for AI-generated fake news? To this end, we conducted a pre-registered, online experiment with N= 988 subjects and 20 fake news from the COVID-19 pandemic generated by GPT-4 vs. humans. Our findings show that AI-generated fake news is perceived as less accurate than human-generated fake news, but both tend to be shared equally. Further, several socio-economic factors explain who falls for AI-generated fake news.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Feb 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 5; 2026: 1.

12 papers.

  1. 2024A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterizationcited by 18
  2. 2024Large language models can consistently generate high-quality content for election disinformation operationscited by 13
  3. 2025Characterizing LLM-driven Social Network: The Chirper.ai Casecited by 9
  4. 2024Lying Blindly: Bypassing ChatGPT's Safeguards to Generate Hard-to-Detect Disinformation Claimscited by 3
  5. 2024METR: Image Watermarking with Large Number of Unique Messagescited by 3
  6. 2024Taxonomy to Regulation: A (Geo)Political Taxonomy for AI Risks and Regulatory Measures in the EU AI Actcited by 1
  7. 2025From Generation to Detection: A Multimodal Multi-Task Dataset for Benchmarking Health Misinformationcited by 1
  8. 2025Designing Effective Digital Literacy Interventions for Boosting Deepfake Discernmentcited by 1
  9. 2026Addressing Data Scarcity in Bangla Fake News Detection: An LLM-Based Dataset Augmentation Approachcited by 1
  10. 2024Evaluating the Propensity of Generative AI for Producing Harmful Disinformation During the 2024 US Election Cyclecited by 0
  11. 2025Images Amplify Misinformation Sharing in Vision-Language Modelscited by 0
  12. 2025Do small language models generate realistic variable-quality fake news headlines?cited by 0