{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:R43H7EJOUOPHAJD4ADYM6NVKPP","short_pith_number":"pith:R43H7EJO","schema_version":"1.0","canonical_sha256":"8f367f912ea39e70247c00f0cf36aa7bd76e44c3e0668ac5d40469f5e47e433d","source":{"kind":"arxiv","id":"2509.11052","version":1},"attestation_state":"computed","paper":{"title":"Commenotes: Synthesizing Organic Comments to Support Community-Based Fact-Checking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Dai Shi, Hewu Li, Jingruo Chen, Linzhi Wang, Shuning Zhang, Xin Yi, Yating Wang, Yifan Wang, Yunyi Chen, Yuwei Chuai","submitted_at":"2025-09-14T02:40:00Z","abstract_excerpt":"Community-based fact-checking is promising to reduce the spread of misleading posts at scale. However, its effectiveness can be undermined by the delays in fact-check delivery. Notably, user-initiated organic comments often contain debunking information and have the potential to help mitigate this limitation. Here, we investigate the feasibility of synthesizing comments to generate timely high-quality fact-checks. To this end, we analyze over 2.2 million replies on X and introduce Commenotes, a two-phase framework that filters and synthesizes comments to facilitate fact-check delivery. Our fra"},"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":"2509.11052","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-09-14T02:40:00Z","cross_cats_sorted":[],"title_canon_sha256":"33bf497cf6207f1f11d86f6fe1f571ca3c799cb5b3aeb40efa99d28c75b0f7a6","abstract_canon_sha256":"29dd025652e93607bccc338b5bc6652960a7c502d2551d77766173d18bd18912"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:11:32.675162Z","signature_b64":"iYxrYkzSMhvTno2+sGwVA6R0glxTsHYpR7oFAv8q3OKTXGT2Pw3J7ix17551dlPWjLK0gqR+wkSAuUSb3hnRAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f367f912ea39e70247c00f0cf36aa7bd76e44c3e0668ac5d40469f5e47e433d","last_reissued_at":"2026-07-05T12:11:32.674669Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:11:32.674669Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Commenotes: Synthesizing Organic Comments to Support Community-Based Fact-Checking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Dai Shi, Hewu Li, Jingruo Chen, Linzhi Wang, Shuning Zhang, Xin Yi, Yating Wang, Yifan Wang, Yunyi Chen, Yuwei Chuai","submitted_at":"2025-09-14T02:40:00Z","abstract_excerpt":"Community-based fact-checking is promising to reduce the spread of misleading posts at scale. However, its effectiveness can be undermined by the delays in fact-check delivery. Notably, user-initiated organic comments often contain debunking information and have the potential to help mitigate this limitation. Here, we investigate the feasibility of synthesizing comments to generate timely high-quality fact-checks. To this end, we analyze over 2.2 million replies on X and introduce Commenotes, a two-phase framework that filters and synthesizes comments to facilitate fact-check delivery. Our fra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.11052","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/2509.11052/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":"2509.11052","created_at":"2026-07-05T12:11:32.674730+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.11052v1","created_at":"2026-07-05T12:11:32.674730+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.11052","created_at":"2026-07-05T12:11:32.674730+00:00"},{"alias_kind":"pith_short_12","alias_value":"R43H7EJOUOPH","created_at":"2026-07-05T12:11:32.674730+00:00"},{"alias_kind":"pith_short_16","alias_value":"R43H7EJOUOPHAJD4","created_at":"2026-07-05T12:11:32.674730+00:00"},{"alias_kind":"pith_short_8","alias_value":"R43H7EJO","created_at":"2026-07-05T12:11:32.674730+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.07101","citing_title":"CANote: Empowering Fact-checking Note Writing Through Scaffolded and Provenance-based Human-AI Collaboration","ref_index":98,"is_internal_anchor":false},{"citing_arxiv_id":"2606.18268","citing_title":"Towards Multi-Agent-Simulation-Based Community Note Evaluation","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30905","citing_title":"How Human Feedback Shapes AI-generated Community Notes","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16566","citing_title":"Characterizing AI Fact-Checkers and Their Contributions on Community Notes","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2507.08110","citing_title":"AI Feedback Enhances Community-Based Content Moderation through Engagement with Counterarguments","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R43H7EJOUOPHAJD4ADYM6NVKPP","json":"https://pith.science/pith/R43H7EJOUOPHAJD4ADYM6NVKPP.json","graph_json":"https://pith.science/api/pith-number/R43H7EJOUOPHAJD4ADYM6NVKPP/graph.json","events_json":"https://pith.science/api/pith-number/R43H7EJOUOPHAJD4ADYM6NVKPP/events.json","paper":"https://pith.science/paper/R43H7EJO"},"agent_actions":{"view_html":"https://pith.science/pith/R43H7EJOUOPHAJD4ADYM6NVKPP","download_json":"https://pith.science/pith/R43H7EJOUOPHAJD4ADYM6NVKPP.json","view_paper":"https://pith.science/paper/R43H7EJO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.11052&json=true","fetch_graph":"https://pith.science/api/pith-number/R43H7EJOUOPHAJD4ADYM6NVKPP/graph.json","fetch_events":"https://pith.science/api/pith-number/R43H7EJOUOPHAJD4ADYM6NVKPP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R43H7EJOUOPHAJD4ADYM6NVKPP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R43H7EJOUOPHAJD4ADYM6NVKPP/action/storage_attestation","attest_author":"https://pith.science/pith/R43H7EJOUOPHAJD4ADYM6NVKPP/action/author_attestation","sign_citation":"https://pith.science/pith/R43H7EJOUOPHAJD4ADYM6NVKPP/action/citation_signature","submit_replication":"https://pith.science/pith/R43H7EJOUOPHAJD4ADYM6NVKPP/action/replication_record"}},"created_at":"2026-07-05T12:11:32.674730+00:00","updated_at":"2026-07-05T12:11:32.674730+00:00"}