{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5K7TFVRJVUSMYUOOL422S7C4R2","short_pith_number":"pith:5K7TFVRJ","schema_version":"1.0","canonical_sha256":"eabf32d629ad24cc51ce5f35a97c5c8eb6a90841f6783510279b4c1ca7fd72b4","source":{"kind":"arxiv","id":"1806.05180","version":1},"attestation_state":"computed","paper":{"title":"A Retrospective Analysis of the Fake News Challenge Stance Detection Task","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.SI"],"primary_cat":"cs.IR","authors_text":"Andreas Hanselowski, Avinesh PVS, Benjamin Schiller, Christian M. Meyer, Debanjan Chaudhuri, Felix Caspelherr, Iryna Gurevych","submitted_at":"2018-06-13T15:38:09Z","abstract_excerpt":"The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance classification task as a crucial first step towards detecting fake news. To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods. In this paper, we provide such an in-depth analysis for the three top-performing systems. We first find that FNC-1's proposed evaluation metric favors the majority class, which can be easily classified, and thus overestimates the true discriminative power of th"},"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":"1806.05180","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-06-13T15:38:09Z","cross_cats_sorted":["cs.AI","cs.CL","cs.SI"],"title_canon_sha256":"9d163b5a91f0925e756db9ac7be2a7ee14ffb54da8fd47f571258a72db3ac75e","abstract_canon_sha256":"42f386715760124b31427943a94b4f07d637714dbadee508c1883067a80786dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:16.837350Z","signature_b64":"E4B/FGZ0f1rgHS4YBbT9pojYu+uCaZwOe85J7ip6n4k6DQhmHfztpHab7LcuHYaS5Z1WknTF1g/kB1nUwoyrDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eabf32d629ad24cc51ce5f35a97c5c8eb6a90841f6783510279b4c1ca7fd72b4","last_reissued_at":"2026-05-18T00:13:16.836697Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:16.836697Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Retrospective Analysis of the Fake News Challenge Stance Detection Task","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.SI"],"primary_cat":"cs.IR","authors_text":"Andreas Hanselowski, Avinesh PVS, Benjamin Schiller, Christian M. Meyer, Debanjan Chaudhuri, Felix Caspelherr, Iryna Gurevych","submitted_at":"2018-06-13T15:38:09Z","abstract_excerpt":"The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance classification task as a crucial first step towards detecting fake news. To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods. In this paper, we provide such an in-depth analysis for the three top-performing systems. We first find that FNC-1's proposed evaluation metric favors the majority class, which can be easily classified, and thus overestimates the true discriminative power of th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.05180","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":""},"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":"1806.05180","created_at":"2026-05-18T00:13:16.836785+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.05180v1","created_at":"2026-05-18T00:13:16.836785+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.05180","created_at":"2026-05-18T00:13:16.836785+00:00"},{"alias_kind":"pith_short_12","alias_value":"5K7TFVRJVUSM","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5K7TFVRJVUSMYUOO","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5K7TFVRJ","created_at":"2026-05-18T12:32:08.215937+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/5K7TFVRJVUSMYUOOL422S7C4R2","json":"https://pith.science/pith/5K7TFVRJVUSMYUOOL422S7C4R2.json","graph_json":"https://pith.science/api/pith-number/5K7TFVRJVUSMYUOOL422S7C4R2/graph.json","events_json":"https://pith.science/api/pith-number/5K7TFVRJVUSMYUOOL422S7C4R2/events.json","paper":"https://pith.science/paper/5K7TFVRJ"},"agent_actions":{"view_html":"https://pith.science/pith/5K7TFVRJVUSMYUOOL422S7C4R2","download_json":"https://pith.science/pith/5K7TFVRJVUSMYUOOL422S7C4R2.json","view_paper":"https://pith.science/paper/5K7TFVRJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.05180&json=true","fetch_graph":"https://pith.science/api/pith-number/5K7TFVRJVUSMYUOOL422S7C4R2/graph.json","fetch_events":"https://pith.science/api/pith-number/5K7TFVRJVUSMYUOOL422S7C4R2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5K7TFVRJVUSMYUOOL422S7C4R2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5K7TFVRJVUSMYUOOL422S7C4R2/action/storage_attestation","attest_author":"https://pith.science/pith/5K7TFVRJVUSMYUOOL422S7C4R2/action/author_attestation","sign_citation":"https://pith.science/pith/5K7TFVRJVUSMYUOOL422S7C4R2/action/citation_signature","submit_replication":"https://pith.science/pith/5K7TFVRJVUSMYUOOL422S7C4R2/action/replication_record"}},"created_at":"2026-05-18T00:13:16.836785+00:00","updated_at":"2026-05-18T00:13:16.836785+00:00"}