{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HOECDVVWEZPYQRHSZ24KB2YKWE","short_pith_number":"pith:HOECDVVW","schema_version":"1.0","canonical_sha256":"3b8821d6b6265f8844f2ceb8a0eb0ab12ad374d963db1d3e8e8b0ebf69c85ca9","source":{"kind":"arxiv","id":"2310.00347","version":3},"attestation_state":"computed","paper":{"title":"Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Abdullah Y Muaad, Oluwanifemi Bamgbose, Shaina Raza, Shardul Ghuge, Veronica Chatrath, Yan Sidyakin","submitted_at":"2023-09-30T12:06:04Z","abstract_excerpt":"Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making. Traditional language models frequently face challenges in generalizing beyond their training data and are typically designed for a single task, often focusing on bias detection at the sentence level. To address this, we present the Contextualized Bi-Directional Dual Transformer (CBDT) \\textcolor{green}{\\faLeaf} classifier. This model combines two complementary transformer networks: the Context Transformer and the Entity Transformer, with a focus on improving bias dete"},"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":"2310.00347","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-09-30T12:06:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"fe30e92d6ec5dac3d2eee184a13b20ab78a670f7f7b1fd169bbae05fab2ef710","abstract_canon_sha256":"8ad3d09ee299aba52f098d801e904a9a445641f2bdad9a03a7cd14835a39d67c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:08:51.602876Z","signature_b64":"LiZNvNVtZv2/HKXAjKpq9L/1wyZuK2mPIuTM2hVt4ZG41M36U2V36rnBA81QAxOQ1bNUYNeF01fdNNaacvhjAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b8821d6b6265f8844f2ceb8a0eb0ab12ad374d963db1d3e8e8b0ebf69c85ca9","last_reissued_at":"2026-07-05T08:08:51.602384Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:08:51.602384Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Abdullah Y Muaad, Oluwanifemi Bamgbose, Shaina Raza, Shardul Ghuge, Veronica Chatrath, Yan Sidyakin","submitted_at":"2023-09-30T12:06:04Z","abstract_excerpt":"Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making. Traditional language models frequently face challenges in generalizing beyond their training data and are typically designed for a single task, often focusing on bias detection at the sentence level. To address this, we present the Contextualized Bi-Directional Dual Transformer (CBDT) \\textcolor{green}{\\faLeaf} classifier. This model combines two complementary transformer networks: the Context Transformer and the Entity Transformer, with a focus on improving bias dete"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00347","kind":"arxiv","version":3},"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/2310.00347/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":"2310.00347","created_at":"2026-07-05T08:08:51.602444+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.00347v3","created_at":"2026-07-05T08:08:51.602444+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00347","created_at":"2026-07-05T08:08:51.602444+00:00"},{"alias_kind":"pith_short_12","alias_value":"HOECDVVWEZPY","created_at":"2026-07-05T08:08:51.602444+00:00"},{"alias_kind":"pith_short_16","alias_value":"HOECDVVWEZPYQRHS","created_at":"2026-07-05T08:08:51.602444+00:00"},{"alias_kind":"pith_short_8","alias_value":"HOECDVVW","created_at":"2026-07-05T08:08:51.602444+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/HOECDVVWEZPYQRHSZ24KB2YKWE","json":"https://pith.science/pith/HOECDVVWEZPYQRHSZ24KB2YKWE.json","graph_json":"https://pith.science/api/pith-number/HOECDVVWEZPYQRHSZ24KB2YKWE/graph.json","events_json":"https://pith.science/api/pith-number/HOECDVVWEZPYQRHSZ24KB2YKWE/events.json","paper":"https://pith.science/paper/HOECDVVW"},"agent_actions":{"view_html":"https://pith.science/pith/HOECDVVWEZPYQRHSZ24KB2YKWE","download_json":"https://pith.science/pith/HOECDVVWEZPYQRHSZ24KB2YKWE.json","view_paper":"https://pith.science/paper/HOECDVVW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.00347&json=true","fetch_graph":"https://pith.science/api/pith-number/HOECDVVWEZPYQRHSZ24KB2YKWE/graph.json","fetch_events":"https://pith.science/api/pith-number/HOECDVVWEZPYQRHSZ24KB2YKWE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HOECDVVWEZPYQRHSZ24KB2YKWE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HOECDVVWEZPYQRHSZ24KB2YKWE/action/storage_attestation","attest_author":"https://pith.science/pith/HOECDVVWEZPYQRHSZ24KB2YKWE/action/author_attestation","sign_citation":"https://pith.science/pith/HOECDVVWEZPYQRHSZ24KB2YKWE/action/citation_signature","submit_replication":"https://pith.science/pith/HOECDVVWEZPYQRHSZ24KB2YKWE/action/replication_record"}},"created_at":"2026-07-05T08:08:51.602444+00:00","updated_at":"2026-07-05T08:08:51.602444+00:00"}