{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DS6DVWH3LWW7E5QASX6NRZGLRY","short_pith_number":"pith:DS6DVWH3","schema_version":"1.0","canonical_sha256":"1cbc3ad8fb5dadf2760095fcd8e4cb8e17ad4c24d2c7294038c0a1df80a7ecda","source":{"kind":"arxiv","id":"2502.13853","version":1},"attestation_state":"computed","paper":{"title":"Fine-grained Fallacy Detection with Human Label Variation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Agnese Daffara, Alan Ramponi, Sara Tonelli","submitted_at":"2025-02-19T16:18:44Z","abstract_excerpt":"We introduce Faina, the first dataset for fallacy detection that embraces multiple plausible answers and natural disagreement. Faina includes over 11K span-level annotations with overlaps across 20 fallacy types on social media posts in Italian about migration, climate change, and public health given by two expert annotators. Through an extensive annotation study that allowed discussion over multiple rounds, we minimize annotation errors whilst keeping signals of human label variation. Moreover, we devise a framework that goes beyond \"single ground truth\" evaluation and simultaneously accounts"},"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":"2502.13853","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-19T16:18:44Z","cross_cats_sorted":[],"title_canon_sha256":"596567654c0df20ec69cb64e5cbef01479dcb3ee7cc951231a9eb34cf51d578e","abstract_canon_sha256":"d5c5d380dba5bd2f7b47139d222f4ff4d1631aaee316be3a1d489d5f06866c67"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:01.513497Z","signature_b64":"JGy1hpBn+IiHIm2WB/XAmzG8X1MSoVR7KOJ9AlL9PqSii5plhc25yFgXwV590fL0TKeoAfiHXKpXM0d2WUOyAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1cbc3ad8fb5dadf2760095fcd8e4cb8e17ad4c24d2c7294038c0a1df80a7ecda","last_reissued_at":"2026-07-05T10:17:01.513004Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:01.513004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fine-grained Fallacy Detection with Human Label Variation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Agnese Daffara, Alan Ramponi, Sara Tonelli","submitted_at":"2025-02-19T16:18:44Z","abstract_excerpt":"We introduce Faina, the first dataset for fallacy detection that embraces multiple plausible answers and natural disagreement. Faina includes over 11K span-level annotations with overlaps across 20 fallacy types on social media posts in Italian about migration, climate change, and public health given by two expert annotators. Through an extensive annotation study that allowed discussion over multiple rounds, we minimize annotation errors whilst keeping signals of human label variation. Moreover, we devise a framework that goes beyond \"single ground truth\" evaluation and simultaneously accounts"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.13853","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/2502.13853/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":"2502.13853","created_at":"2026-07-05T10:17:01.513062+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.13853v1","created_at":"2026-07-05T10:17:01.513062+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.13853","created_at":"2026-07-05T10:17:01.513062+00:00"},{"alias_kind":"pith_short_12","alias_value":"DS6DVWH3LWW7","created_at":"2026-07-05T10:17:01.513062+00:00"},{"alias_kind":"pith_short_16","alias_value":"DS6DVWH3LWW7E5QA","created_at":"2026-07-05T10:17:01.513062+00:00"},{"alias_kind":"pith_short_8","alias_value":"DS6DVWH3","created_at":"2026-07-05T10:17:01.513062+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01168","citing_title":"Quantifying and Predicting Disagreement in Graded Human Ratings","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18069","citing_title":"Modeling Human Perspectives with Socio-Demographic Representations","ref_index":39,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DS6DVWH3LWW7E5QASX6NRZGLRY","json":"https://pith.science/pith/DS6DVWH3LWW7E5QASX6NRZGLRY.json","graph_json":"https://pith.science/api/pith-number/DS6DVWH3LWW7E5QASX6NRZGLRY/graph.json","events_json":"https://pith.science/api/pith-number/DS6DVWH3LWW7E5QASX6NRZGLRY/events.json","paper":"https://pith.science/paper/DS6DVWH3"},"agent_actions":{"view_html":"https://pith.science/pith/DS6DVWH3LWW7E5QASX6NRZGLRY","download_json":"https://pith.science/pith/DS6DVWH3LWW7E5QASX6NRZGLRY.json","view_paper":"https://pith.science/paper/DS6DVWH3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.13853&json=true","fetch_graph":"https://pith.science/api/pith-number/DS6DVWH3LWW7E5QASX6NRZGLRY/graph.json","fetch_events":"https://pith.science/api/pith-number/DS6DVWH3LWW7E5QASX6NRZGLRY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DS6DVWH3LWW7E5QASX6NRZGLRY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DS6DVWH3LWW7E5QASX6NRZGLRY/action/storage_attestation","attest_author":"https://pith.science/pith/DS6DVWH3LWW7E5QASX6NRZGLRY/action/author_attestation","sign_citation":"https://pith.science/pith/DS6DVWH3LWW7E5QASX6NRZGLRY/action/citation_signature","submit_replication":"https://pith.science/pith/DS6DVWH3LWW7E5QASX6NRZGLRY/action/replication_record"}},"created_at":"2026-07-05T10:17:01.513062+00:00","updated_at":"2026-07-05T10:17:01.513062+00:00"}