Dungeons & Deepfakes: Using scenario-based role-play to study journalists' behavior towards using AI-based verification tools for video content
Saniat Javid Sohrawardi, Y. Kelly Wu, Andrea Hickerson, Matthew Wright
Abstract
The evolving landscape of manipulated media, including the threat of deepfakes, has made information verification a daunting challenge for journalists. Technologists have developed tools to detect deepfakes, but these tools can sometimes yield inaccurate results, raising concerns about inadvertently disseminating manipulated content as authentic news. This study examines the impact of unreliable deepfake detection tools on information verification. We conducted role-playing exercises with 24 US journalists, immersing them in complex breaking-news scenarios where determining authenticity was challenging. Through these exercises, we explored questions regarding journalists’ investigative processes, use of a deepfake detection tool, and decisions on when and what to publish. Our findings reveal that journalists are diligent in verifying information, but sometimes rely too heavily on results from deepfake detection tools. We argue for more cautious release of such tools, accompanied by proper training for users to mitigate the risk of unintentionally propagating manipulated content as real news.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Cited by top-tier papers4
- To Rely or Not to Rely? Evaluating Interventions for Appropriate Reliance on Large Language ModelsJessica Y. Bo, Sophia Wan, Ashton AndersonCHI 2025 · 31 citations
- How Do HCI Researchers Study Cognitive Biases? A Scoping ReviewNattapat Boonprakong, Benjamin Tag, Jorge Gonçalves, Tilman DinglerCHI 2025 · 19 citations
- Understanding and Empowering Intelligence Analysts: User-Centered Design for Deepfake Detection ToolsY. Kelly Wu, Saniat Javid Sohrawardi, Candice R. Gerstner, Matthew WrightCHI 2025 · 7 citations
- Collab: Fostering Critical Identification of Deepfake Videos on Social Media via Synergistic AnnotationShuning Zhang, Linzhi Wang, Shixuan Li, Yuanyuan Wu et al.CHI 2026 · 1 citation
Related papers
- Beyond the Naked Eye: Empirical Study of How People Perceive, Detect, and Respond to AI-Manipulated VideosKaniz Fatima, Y. Kelly Wu, Ersin UzunCHI 2026 · 1 citation
- DeepPhish: Understanding User Trust Towards Artificially Generated Profiles in Online Social NetworksJaron Mink, Licheng Luo, Natã M. Barbosa, Olivia Figueira et al.USENIX Security 2022
- That's Fake News! Reliability of News When Provided Title, Image, Source Bias & Full ArticleFrancesca Spezzano, Anu Shrestha, Jerry Alan Fails, Brian W. StoneCSCW 2021 · 22 citations
- Fake News on Facebook and Twitter: Investigating How People (Don't) InvestigateChristine Geeng, Savanna Yee, Franziska RoesnerCHI 2020 · 154 citations
- Celeb-DF: A Large-Scale Challenging Dataset for DeepFake ForensicsYuezun Li, Xin Yang, Pu Sun, Honggang Qi et al.CVPR 2020
