{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DD3GKZLC3FI5II2WV5FKVUZ6DQ","short_pith_number":"pith:DD3GKZLC","schema_version":"1.0","canonical_sha256":"18f6656562d951d42356af4aaad33e1c3bec5a2be2e2a2d7c47a2544dc5a85f1","source":{"kind":"arxiv","id":"2309.15847","version":1},"attestation_state":"computed","paper":{"title":"Disinformation Detection: An Evolving Challenge in the Age of LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.CL","authors_text":"Ayushi Nirmal, Bohan Jiang, Huan Liu, Zhen Tan","submitted_at":"2023-09-25T22:12:50Z","abstract_excerpt":"The advent of generative Large Language Models (LLMs) such as ChatGPT has catalyzed transformative advancements across multiple domains. However, alongside these advancements, they have also introduced potential threats. One critical concern is the misuse of LLMs by disinformation spreaders, leveraging these models to generate highly persuasive yet misleading content that challenges the disinformation detection system. This work aims to address this issue by answering three research questions: (1) To what extent can the current disinformation detection technique reliably detect LLM-generated d"},"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":"2309.15847","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-09-25T22:12:50Z","cross_cats_sorted":["cs.AI","cs.CY"],"title_canon_sha256":"5a23ff0970944e525319e51a9ee800dae1e5ab9cf3699f27a2241321c407f4d9","abstract_canon_sha256":"c8d64d44217c1fee2d8f8fb3376199b2eea3801260af04ffe680feadbef28523"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:54:58.501150Z","signature_b64":"Iikeal5ffE5zOyRP/fIGI0lcbd52tJaig2ypR4B+AnWRipWt1/fcsbxtLsxNCWu8H4A/ZitpQZS6fi1XGIuvDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"18f6656562d951d42356af4aaad33e1c3bec5a2be2e2a2d7c47a2544dc5a85f1","last_reissued_at":"2026-07-05T06:54:58.500808Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:54:58.500808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Disinformation Detection: An Evolving Challenge in the Age of LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.CL","authors_text":"Ayushi Nirmal, Bohan Jiang, Huan Liu, Zhen Tan","submitted_at":"2023-09-25T22:12:50Z","abstract_excerpt":"The advent of generative Large Language Models (LLMs) such as ChatGPT has catalyzed transformative advancements across multiple domains. However, alongside these advancements, they have also introduced potential threats. One critical concern is the misuse of LLMs by disinformation spreaders, leveraging these models to generate highly persuasive yet misleading content that challenges the disinformation detection system. This work aims to address this issue by answering three research questions: (1) To what extent can the current disinformation detection technique reliably detect LLM-generated d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.15847","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/2309.15847/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":"2309.15847","created_at":"2026-07-05T06:54:58.500856+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.15847v1","created_at":"2026-07-05T06:54:58.500856+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.15847","created_at":"2026-07-05T06:54:58.500856+00:00"},{"alias_kind":"pith_short_12","alias_value":"DD3GKZLC3FI5","created_at":"2026-07-05T06:54:58.500856+00:00"},{"alias_kind":"pith_short_16","alias_value":"DD3GKZLC3FI5II2W","created_at":"2026-07-05T06:54:58.500856+00:00"},{"alias_kind":"pith_short_8","alias_value":"DD3GKZLC","created_at":"2026-07-05T06:54:58.500856+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.18649","citing_title":"Fake News Detection After LLM Laundering: Measurement and Explanation","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DD3GKZLC3FI5II2WV5FKVUZ6DQ","json":"https://pith.science/pith/DD3GKZLC3FI5II2WV5FKVUZ6DQ.json","graph_json":"https://pith.science/api/pith-number/DD3GKZLC3FI5II2WV5FKVUZ6DQ/graph.json","events_json":"https://pith.science/api/pith-number/DD3GKZLC3FI5II2WV5FKVUZ6DQ/events.json","paper":"https://pith.science/paper/DD3GKZLC"},"agent_actions":{"view_html":"https://pith.science/pith/DD3GKZLC3FI5II2WV5FKVUZ6DQ","download_json":"https://pith.science/pith/DD3GKZLC3FI5II2WV5FKVUZ6DQ.json","view_paper":"https://pith.science/paper/DD3GKZLC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.15847&json=true","fetch_graph":"https://pith.science/api/pith-number/DD3GKZLC3FI5II2WV5FKVUZ6DQ/graph.json","fetch_events":"https://pith.science/api/pith-number/DD3GKZLC3FI5II2WV5FKVUZ6DQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DD3GKZLC3FI5II2WV5FKVUZ6DQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DD3GKZLC3FI5II2WV5FKVUZ6DQ/action/storage_attestation","attest_author":"https://pith.science/pith/DD3GKZLC3FI5II2WV5FKVUZ6DQ/action/author_attestation","sign_citation":"https://pith.science/pith/DD3GKZLC3FI5II2WV5FKVUZ6DQ/action/citation_signature","submit_replication":"https://pith.science/pith/DD3GKZLC3FI5II2WV5FKVUZ6DQ/action/replication_record"}},"created_at":"2026-07-05T06:54:58.500856+00:00","updated_at":"2026-07-05T06:54:58.500856+00:00"}