{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:I7CHGWEA7GPZ4ZHC4BPAON65VA","short_pith_number":"pith:I7CHGWEA","schema_version":"1.0","canonical_sha256":"47c4735880f99f9e64e2e05e0737dda820da09e9d90035eacad3ae35f42f96d9","source":{"kind":"arxiv","id":"2202.08159","version":1},"attestation_state":"computed","paper":{"title":"Domain Adaptive Fake News Detection via Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Ahmadreza Mosallanezhad, Huan Liu, Kai Shu, Mansooreh Karami, Michelle V. Mancenido","submitted_at":"2022-02-16T16:05:37Z","abstract_excerpt":"With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news. Effective fake news detection is a non-trivial task due to the diverse nature of news domains and expensive annotation costs. In this work, we address the limitations of existing automated fake news detection models by incorporating auxiliary information (e.g., user comments and user-news interactions) into a novel reinforcement learning-based model called \\textbf{RE}inforced \\textbf{A}daptive \\textbf{"},"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":"2202.08159","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2022-02-16T16:05:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"582ecccfc8a3f709041867adbdc535d9f11603352e7d1f5550e0b7ceec67d3df","abstract_canon_sha256":"ffcfc67b9bbfb1ce9af3cd4d1c2f7a113d09a6ed2a9b48d67059e8f3d4a191e3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:57:37.573096Z","signature_b64":"S3p0kTFMFXWPU6J40afluTZRiU67YfczxrxPVqcSygVwOqYwag/DcbiYkhKP1g2FwV6CQ9DMb+6Uw/Onu5hvAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47c4735880f99f9e64e2e05e0737dda820da09e9d90035eacad3ae35f42f96d9","last_reissued_at":"2026-07-05T03:57:37.572649Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:57:37.572649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Domain Adaptive Fake News Detection via Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Ahmadreza Mosallanezhad, Huan Liu, Kai Shu, Mansooreh Karami, Michelle V. Mancenido","submitted_at":"2022-02-16T16:05:37Z","abstract_excerpt":"With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news. Effective fake news detection is a non-trivial task due to the diverse nature of news domains and expensive annotation costs. In this work, we address the limitations of existing automated fake news detection models by incorporating auxiliary information (e.g., user comments and user-news interactions) into a novel reinforcement learning-based model called \\textbf{RE}inforced \\textbf{A}daptive \\textbf{"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.08159","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/2202.08159/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":"2202.08159","created_at":"2026-07-05T03:57:37.572706+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.08159v1","created_at":"2026-07-05T03:57:37.572706+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.08159","created_at":"2026-07-05T03:57:37.572706+00:00"},{"alias_kind":"pith_short_12","alias_value":"I7CHGWEA7GPZ","created_at":"2026-07-05T03:57:37.572706+00:00"},{"alias_kind":"pith_short_16","alias_value":"I7CHGWEA7GPZ4ZHC","created_at":"2026-07-05T03:57:37.572706+00:00"},{"alias_kind":"pith_short_8","alias_value":"I7CHGWEA","created_at":"2026-07-05T03:57:37.572706+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I7CHGWEA7GPZ4ZHC4BPAON65VA","json":"https://pith.science/pith/I7CHGWEA7GPZ4ZHC4BPAON65VA.json","graph_json":"https://pith.science/api/pith-number/I7CHGWEA7GPZ4ZHC4BPAON65VA/graph.json","events_json":"https://pith.science/api/pith-number/I7CHGWEA7GPZ4ZHC4BPAON65VA/events.json","paper":"https://pith.science/paper/I7CHGWEA"},"agent_actions":{"view_html":"https://pith.science/pith/I7CHGWEA7GPZ4ZHC4BPAON65VA","download_json":"https://pith.science/pith/I7CHGWEA7GPZ4ZHC4BPAON65VA.json","view_paper":"https://pith.science/paper/I7CHGWEA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.08159&json=true","fetch_graph":"https://pith.science/api/pith-number/I7CHGWEA7GPZ4ZHC4BPAON65VA/graph.json","fetch_events":"https://pith.science/api/pith-number/I7CHGWEA7GPZ4ZHC4BPAON65VA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I7CHGWEA7GPZ4ZHC4BPAON65VA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I7CHGWEA7GPZ4ZHC4BPAON65VA/action/storage_attestation","attest_author":"https://pith.science/pith/I7CHGWEA7GPZ4ZHC4BPAON65VA/action/author_attestation","sign_citation":"https://pith.science/pith/I7CHGWEA7GPZ4ZHC4BPAON65VA/action/citation_signature","submit_replication":"https://pith.science/pith/I7CHGWEA7GPZ4ZHC4BPAON65VA/action/replication_record"}},"created_at":"2026-07-05T03:57:37.572706+00:00","updated_at":"2026-07-05T03:57:37.572706+00:00"}