CrisisFake: Benchmark Validity of AI-Generated Text Detection for Disaster Social Sensing
Disaster social sensing converts public social-media posts into evidence for situational awareness and humanitarian response, but plausible LLM-generated posts can contaminate this information stream and distort assessments of needs, damage, and resource priorities. This study empirically investigates whether text-based detectors can distinguish human-authored from LLM-generated disaster posts and what textual cues underlie their judgments. We construct CrisisFake, a Qwen2.5-7B-based dataset of 12,000 texts organized into 3,000 matched semantic units from nine disasters. Each unit contains an original human post, a minimally LLM-proofread human post, a fact-preserving LLM-generated post p...
| Authors | Xiaoshan Zhou, Zaifu Zhan |
|---|---|
| Primary category | cs.CL |
| Published | 21 September 2026 |
| Last updated at source | 7 October 2026 |
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