{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TA6WN7YYSVV2PH734AXXHLWGDM","short_pith_number":"pith:TA6WN7YY","schema_version":"1.0","canonical_sha256":"983d66ff18956ba79ffbe02f73aec61b2597f498117737300feff4dbe7875007","source":{"kind":"arxiv","id":"1911.11951","version":1},"attestation_state":"computed","paper":{"title":"Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexander Wong, Chris Dulhanty, Ibrahim Ben Daya, Jason L. Deglint","submitted_at":"2019-11-27T04:52:53Z","abstract_excerpt":"The exponential rise of social media and digital news in the past decade has had the unfortunate consequence of escalating what the United Nations has called a global topic of concern: the growing prevalence of disinformation. Given the complexity and time-consuming nature of combating disinformation through human assessment, one is motivated to explore harnessing AI solutions to automatically assess news articles for the presence of disinformation. A valuable first step towards automatic identification of disinformation is stance detection, where given a claim and a news article, the aim is t"},"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":"1911.11951","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-27T04:52:53Z","cross_cats_sorted":[],"title_canon_sha256":"88aece409d87a3a0eaadca6e99ef07ff67fcef09b3713fe21cced7e87b064df6","abstract_canon_sha256":"07f6e4a81a14ded261fb606c66899aa7d658ac9bccc4cc7f2a514c3fc5114650"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:22:28.973629Z","signature_b64":"K6apzA6G31q+8oaxjTHpcoWgNUhvUaICfbsX2InFkysx/YrDnB+K2za4jIDX2m44UsypQKlDctS7jWnXsrOrAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"983d66ff18956ba79ffbe02f73aec61b2597f498117737300feff4dbe7875007","last_reissued_at":"2026-07-05T00:22:28.973217Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:22:28.973217Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexander Wong, Chris Dulhanty, Ibrahim Ben Daya, Jason L. Deglint","submitted_at":"2019-11-27T04:52:53Z","abstract_excerpt":"The exponential rise of social media and digital news in the past decade has had the unfortunate consequence of escalating what the United Nations has called a global topic of concern: the growing prevalence of disinformation. Given the complexity and time-consuming nature of combating disinformation through human assessment, one is motivated to explore harnessing AI solutions to automatically assess news articles for the presence of disinformation. A valuable first step towards automatic identification of disinformation is stance detection, where given a claim and a news article, the aim is t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.11951","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/1911.11951/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":"1911.11951","created_at":"2026-07-05T00:22:28.973272+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.11951v1","created_at":"2026-07-05T00:22:28.973272+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.11951","created_at":"2026-07-05T00:22:28.973272+00:00"},{"alias_kind":"pith_short_12","alias_value":"TA6WN7YYSVV2","created_at":"2026-07-05T00:22:28.973272+00:00"},{"alias_kind":"pith_short_16","alias_value":"TA6WN7YYSVV2PH73","created_at":"2026-07-05T00:22:28.973272+00:00"},{"alias_kind":"pith_short_8","alias_value":"TA6WN7YY","created_at":"2026-07-05T00:22:28.973272+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.04375","citing_title":"Large Language Models and Social Media Information Integrity: Opportunities, Challenges, and Research Directions","ref_index":51,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TA6WN7YYSVV2PH734AXXHLWGDM","json":"https://pith.science/pith/TA6WN7YYSVV2PH734AXXHLWGDM.json","graph_json":"https://pith.science/api/pith-number/TA6WN7YYSVV2PH734AXXHLWGDM/graph.json","events_json":"https://pith.science/api/pith-number/TA6WN7YYSVV2PH734AXXHLWGDM/events.json","paper":"https://pith.science/paper/TA6WN7YY"},"agent_actions":{"view_html":"https://pith.science/pith/TA6WN7YYSVV2PH734AXXHLWGDM","download_json":"https://pith.science/pith/TA6WN7YYSVV2PH734AXXHLWGDM.json","view_paper":"https://pith.science/paper/TA6WN7YY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.11951&json=true","fetch_graph":"https://pith.science/api/pith-number/TA6WN7YYSVV2PH734AXXHLWGDM/graph.json","fetch_events":"https://pith.science/api/pith-number/TA6WN7YYSVV2PH734AXXHLWGDM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TA6WN7YYSVV2PH734AXXHLWGDM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TA6WN7YYSVV2PH734AXXHLWGDM/action/storage_attestation","attest_author":"https://pith.science/pith/TA6WN7YYSVV2PH734AXXHLWGDM/action/author_attestation","sign_citation":"https://pith.science/pith/TA6WN7YYSVV2PH734AXXHLWGDM/action/citation_signature","submit_replication":"https://pith.science/pith/TA6WN7YYSVV2PH734AXXHLWGDM/action/replication_record"}},"created_at":"2026-07-05T00:22:28.973272+00:00","updated_at":"2026-07-05T00:22:28.973272+00:00"}