Can We Still Trust Disaster Social Sensing? Empirical Evidence on Detecting AI-Generated Social Media Posts

A research study investigates the reliability of disaster social sensing in the context of generative Artificial Intelligence (AI) by examining whether text-based AI detectors can differentiate between human-authored and AI-generated social media posts. The study constructs a comprehensive dataset of 12,000 texts related to nine disasters, including human posts, AI-proofread human posts, factual AI-generated posts, and affectively framed AI posts, to test the efficacy of current detection methods. This research addresses a critical emerging challenge to information integrity in disaster response.

The increasing sophistication of generative AI poses a significant threat to the integrity of real-time information systems, particularly those reliant on public input like disaster social sensing. Organisations must understand the limitations of current detection technologies to safeguard decision-making processes, ensure accurate situational awareness, and maintain trust in information channels during critical events.

Generative AI can produce plausible social media messages resembling eyewitness reports, potentially compromising disaster social sensing.

Forward consideration, not a verified fact.