{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:O4CLT5F3EVZ25XQBJE3MD5VRXW","short_pith_number":"pith:O4CLT5F3","schema_version":"1.0","canonical_sha256":"7704b9f4bb2573aede014936c1f6b1bd8e58b25e9d5d6387b12385360168a8fb","source":{"kind":"arxiv","id":"2506.09975","version":2},"attestation_state":"computed","paper":{"title":"When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hillary Dawkins, Kathleen C. Fraser, Svetlana Kiritchenko","submitted_at":"2025-06-11T17:51:28Z","abstract_excerpt":"Detecting AI-generated text is a difficult problem to begin with; detecting AI-generated text on social media is made even more difficult due to the short text length and informal, idiosyncratic language of the internet. It is nonetheless important to tackle this problem, as social media represents a significant attack vector in online influence campaigns, which may be bolstered through the use of mass-produced AI-generated posts supporting (or opposing) particular policies, decisions, or events. We approach this problem with the mindset and resources of a reasonably sophisticated threat actor"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.09975","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-11T17:51:28Z","cross_cats_sorted":[],"title_canon_sha256":"0dda193243d56b8d1e0a7fb22abb288f86e28eebee43e72023f283fbf27045d6","abstract_canon_sha256":"107a6b3450a515ddfdce49f10605e3c10d0dd5bddc02855ade19368accccf997"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:19.028503Z","signature_b64":"HAWzUOAJ3APpcSjDx66eyHVFBi+7Ranb46i+Pgxxz6xQiTUwv0Gysa9SvT3LxiqQTlaLKAdoLEpPTYNCSXCkCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7704b9f4bb2573aede014936c1f6b1bd8e58b25e9d5d6387b12385360168a8fb","last_reissued_at":"2026-07-05T11:22:19.027979Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:19.027979Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hillary Dawkins, Kathleen C. Fraser, Svetlana Kiritchenko","submitted_at":"2025-06-11T17:51:28Z","abstract_excerpt":"Detecting AI-generated text is a difficult problem to begin with; detecting AI-generated text on social media is made even more difficult due to the short text length and informal, idiosyncratic language of the internet. It is nonetheless important to tackle this problem, as social media represents a significant attack vector in online influence campaigns, which may be bolstered through the use of mass-produced AI-generated posts supporting (or opposing) particular policies, decisions, or events. We approach this problem with the mindset and resources of a reasonably sophisticated threat actor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.09975","kind":"arxiv","version":2},"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/2506.09975/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.09975","created_at":"2026-07-05T11:22:19.028043+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.09975v2","created_at":"2026-07-05T11:22:19.028043+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.09975","created_at":"2026-07-05T11:22:19.028043+00:00"},{"alias_kind":"pith_short_12","alias_value":"O4CLT5F3EVZ2","created_at":"2026-07-05T11:22:19.028043+00:00"},{"alias_kind":"pith_short_16","alias_value":"O4CLT5F3EVZ25XQB","created_at":"2026-07-05T11:22:19.028043+00:00"},{"alias_kind":"pith_short_8","alias_value":"O4CLT5F3","created_at":"2026-07-05T11:22:19.028043+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25152","citing_title":"Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O4CLT5F3EVZ25XQBJE3MD5VRXW","json":"https://pith.science/pith/O4CLT5F3EVZ25XQBJE3MD5VRXW.json","graph_json":"https://pith.science/api/pith-number/O4CLT5F3EVZ25XQBJE3MD5VRXW/graph.json","events_json":"https://pith.science/api/pith-number/O4CLT5F3EVZ25XQBJE3MD5VRXW/events.json","paper":"https://pith.science/paper/O4CLT5F3"},"agent_actions":{"view_html":"https://pith.science/pith/O4CLT5F3EVZ25XQBJE3MD5VRXW","download_json":"https://pith.science/pith/O4CLT5F3EVZ25XQBJE3MD5VRXW.json","view_paper":"https://pith.science/paper/O4CLT5F3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.09975&json=true","fetch_graph":"https://pith.science/api/pith-number/O4CLT5F3EVZ25XQBJE3MD5VRXW/graph.json","fetch_events":"https://pith.science/api/pith-number/O4CLT5F3EVZ25XQBJE3MD5VRXW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O4CLT5F3EVZ25XQBJE3MD5VRXW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O4CLT5F3EVZ25XQBJE3MD5VRXW/action/storage_attestation","attest_author":"https://pith.science/pith/O4CLT5F3EVZ25XQBJE3MD5VRXW/action/author_attestation","sign_citation":"https://pith.science/pith/O4CLT5F3EVZ25XQBJE3MD5VRXW/action/citation_signature","submit_replication":"https://pith.science/pith/O4CLT5F3EVZ25XQBJE3MD5VRXW/action/replication_record"}},"created_at":"2026-07-05T11:22:19.028043+00:00","updated_at":"2026-07-05T11:22:19.028043+00:00"}