{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:UBV4XWJD3QF7SMNCNKQOXKFRZJ","short_pith_number":"pith:UBV4XWJD","schema_version":"1.0","canonical_sha256":"a06bcbd923dc0bf931a26aa0eba8b1ca44ead67a44bba6b3f78910681c74ce06","source":{"kind":"arxiv","id":"2212.09683","version":4},"attestation_state":"computed","paper":{"title":"Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alan Ritter, Ethan Mendes, Wei Xu, Yang Chen","submitted_at":"2022-12-19T18:11:10Z","abstract_excerpt":"We present a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them. Our approach extracts check-worthy claims, which are aggregated and ranked for review. Stance classifiers are then used to identify tweets supporting novel misinformation claims, which are further reviewed to determine whether they violate relevant policies. To demonstrate the feasibility of our approach, we develop a baseline system based on modern NLP methods for human-in-the-loop fact-checking in the domain of COVID-19 treatments. We make"},"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":"2212.09683","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-12-19T18:11:10Z","cross_cats_sorted":[],"title_canon_sha256":"598bdb8eea5377ab913d998b13e626633b2325b7e5792a616e4f3f2fff718969","abstract_canon_sha256":"4ee68b824b9349134eba803e14a784dd7aee6d590ee898f7bcb2e99b0bd4a181"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:26:44.660766Z","signature_b64":"beBXIy0AvOFv4v2Yl6StCisR5J1635ByPsEF8ogJPlmurRHWnkdN6TN0/lsQrsHF5RsElp4t5gHjhxVLqcEjBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a06bcbd923dc0bf931a26aa0eba8b1ca44ead67a44bba6b3f78910681c74ce06","last_reissued_at":"2026-07-05T06:26:44.659692Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:26:44.659692Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alan Ritter, Ethan Mendes, Wei Xu, Yang Chen","submitted_at":"2022-12-19T18:11:10Z","abstract_excerpt":"We present a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them. Our approach extracts check-worthy claims, which are aggregated and ranked for review. Stance classifiers are then used to identify tweets supporting novel misinformation claims, which are further reviewed to determine whether they violate relevant policies. To demonstrate the feasibility of our approach, we develop a baseline system based on modern NLP methods for human-in-the-loop fact-checking in the domain of COVID-19 treatments. We make"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09683","kind":"arxiv","version":4},"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/2212.09683/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":"2212.09683","created_at":"2026-07-05T06:26:44.660239+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.09683v4","created_at":"2026-07-05T06:26:44.660239+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09683","created_at":"2026-07-05T06:26:44.660239+00:00"},{"alias_kind":"pith_short_12","alias_value":"UBV4XWJD3QF7","created_at":"2026-07-05T06:26:44.660239+00:00"},{"alias_kind":"pith_short_16","alias_value":"UBV4XWJD3QF7SMNC","created_at":"2026-07-05T06:26:44.660239+00:00"},{"alias_kind":"pith_short_8","alias_value":"UBV4XWJD","created_at":"2026-07-05T06:26:44.660239+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.04666","citing_title":"Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy","ref_index":32,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UBV4XWJD3QF7SMNCNKQOXKFRZJ","json":"https://pith.science/pith/UBV4XWJD3QF7SMNCNKQOXKFRZJ.json","graph_json":"https://pith.science/api/pith-number/UBV4XWJD3QF7SMNCNKQOXKFRZJ/graph.json","events_json":"https://pith.science/api/pith-number/UBV4XWJD3QF7SMNCNKQOXKFRZJ/events.json","paper":"https://pith.science/paper/UBV4XWJD"},"agent_actions":{"view_html":"https://pith.science/pith/UBV4XWJD3QF7SMNCNKQOXKFRZJ","download_json":"https://pith.science/pith/UBV4XWJD3QF7SMNCNKQOXKFRZJ.json","view_paper":"https://pith.science/paper/UBV4XWJD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.09683&json=true","fetch_graph":"https://pith.science/api/pith-number/UBV4XWJD3QF7SMNCNKQOXKFRZJ/graph.json","fetch_events":"https://pith.science/api/pith-number/UBV4XWJD3QF7SMNCNKQOXKFRZJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UBV4XWJD3QF7SMNCNKQOXKFRZJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UBV4XWJD3QF7SMNCNKQOXKFRZJ/action/storage_attestation","attest_author":"https://pith.science/pith/UBV4XWJD3QF7SMNCNKQOXKFRZJ/action/author_attestation","sign_citation":"https://pith.science/pith/UBV4XWJD3QF7SMNCNKQOXKFRZJ/action/citation_signature","submit_replication":"https://pith.science/pith/UBV4XWJD3QF7SMNCNKQOXKFRZJ/action/replication_record"}},"created_at":"2026-07-05T06:26:44.660239+00:00","updated_at":"2026-07-05T06:26:44.660239+00:00"}