{"paper":{"title":"When Machines Lie Differently: Detecting AI vs Human Fake News","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Aya-Vera Jimenez, Canliv Ibenye, Dhrubajyoti Ghosh, Samuel Jaeger","submitted_at":"2026-07-24T04:27:31Z","abstract_excerpt":"The rapid advancement of large language models has introduced AI-generated fake news alongside traditional human-written misinformation, raising questions about whether detectability depends on the source of deceptive content. This study examines that issue through two controlled binary classification tasks: distinguishing real news from human-written fake news and from AI-generated fake news. Each article is represented using features related to lexical diversity, readability, and emotional characteristics, and evaluated with several machine learning models, including logistic regression, ran"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.21967","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.21967/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}