Metraipolis › archive › dc_7acff3d76fb5

Can We Still Trust Disaster Social Sensing? Empirical Evidence on Detecting AI-Generated Social Media Posts — arXiv:2609.35821

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.35821
License (as archived): CC0-1.0
Archived: 2026-10-07T16:56:18.605Z
SHA-256 of the archived text: 6341865ec3288570e3f917a3da8a685984c8154089ed60ff93a1444b9e1b08ee
Stable URL of this page: https://metraipolis.com/documento/dc_7acff3d76fb5

How to cite

Can We Still Trust Disaster Social Sensing? Empirical Evidence on Detecting AI-Generated Social Media Posts — arXiv:2609.35821 — arXiv (en), https://arxiv.org/abs/2609.35821 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7acff3d76fb5 (Metraipolis archive dc_7acff3d76fb5, sha256 6341865ec3288570e3f917a3da8a685984c8154089ed60ff93a1444b9e1b08ee)

BibTeX

@misc{dc7acff3d76fb5,
  title = {Can We Still Trust Disaster Social Sensing? Empirical Evidence on Detecting AI-Generated Social Media Posts — arXiv:2609.35821},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.35821},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7acff3d76fb5; sha256 6341865ec3288570e3f917a3da8a685984c8154089ed60ff93a1444b9e1b08ee},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.35821. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Disaster social sensing converts public social-media posts into evidence for situational awareness and humanitarian needs, but generative artificial intelligence (AI) can produce plausible messages that resemble eyewitness reports. This study investigates whether text-based AI detectors can reliably distinguish human-authored from AI-generated disaster posts. We construct a dataset of 12,000 texts organised into 3,000 matched semantic units from nine disasters: original human posts (H0), minimally LLM-proofread human posts (H1), factual AI-generated posts based on the same verified facts (A0), and affectively framed versions of those AI posts (A1). A separate 6,000-text corpus from 42 events supports model selection and threshold calibration. We evaluate OSM-Det, Fast-DetectGPT, Binoculars, and direct large language model (LLM) judges across five model families, then test disaster-domain calibration, a frozen-encoder linear readout, paired transformation sensitivity, and dataset artifact controls. Across fourteen frozen cross-family configurations, AUROC is 0.402-0.517 and the best prospective recall at a calibration-derived low-false-positive operating point is 3.6%; OSM-Det reaches AUROC 0.521 and 10.4% recall at a realised 6.7% false-positive rate. A disaster-trained linear head reaches AUROC 0.817, but a seven-feature surface classifier reaches 0.784 on the H0-versus-A0 contrast, and neutralising identified surface asymmetries reduces the head from 0.733 to 0.594. The head also separates A0 from A1 even though provenance is unchanged. The results show that text-based detection is not reliable enough to serve as an operational trust gate; multimodal claims, accountable sources, and other contextual evidence should be rested on to safeguard trust in disaster social sensing.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-30 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2609.35821 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T16:27:03.154Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).