{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HLNBP4BZPKZ6KH4QCBHXPJNJZ7","short_pith_number":"pith:HLNBP4BZ","schema_version":"1.0","canonical_sha256":"3ada17f0397ab3e51f90104f77a5a9cfda6c5207bb896aaeab46217640a04237","source":{"kind":"arxiv","id":"2404.01722","version":1},"attestation_state":"computed","paper":{"title":"Sentence-level Media Bias Analysis with Event Relation Graph","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ruihong Huang, Yuanyuan Lei","submitted_at":"2024-04-02T08:16:03Z","abstract_excerpt":"Media outlets are becoming more partisan and polarized nowadays. In this paper, we identify media bias at the sentence level, and pinpoint bias sentences that intend to sway readers' opinions. As bias sentences are often expressed in a neutral and factual way, considering broader context outside a sentence can help reveal the bias. In particular, we observe that events in a bias sentence need to be understood in associations with other events in the document. Therefore, we propose to construct an event relation graph to explicitly reason about event-event relations for sentence-level bias iden"},"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":"2404.01722","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-04-02T08:16:03Z","cross_cats_sorted":[],"title_canon_sha256":"87f69f0247a05e55c515449fe51cd05ccfe978f728a17a1696a8cdbdbc33c9f8","abstract_canon_sha256":"13eea48388d59e475def014faec7f613c24ca3fa081d5547f6b9a21912067aeb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:03:18.770224Z","signature_b64":"eMEqbi5vcCk+N7KCyYOK4TJ0RKgIcVwaeSNRvSYXVAmGvpMlgJqvP8uVbLuvLQ73r91IGrzqrgPhzT5Z7C5GAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ada17f0397ab3e51f90104f77a5a9cfda6c5207bb896aaeab46217640a04237","last_reissued_at":"2026-07-05T08:03:18.769788Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:03:18.769788Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sentence-level Media Bias Analysis with Event Relation Graph","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ruihong Huang, Yuanyuan Lei","submitted_at":"2024-04-02T08:16:03Z","abstract_excerpt":"Media outlets are becoming more partisan and polarized nowadays. In this paper, we identify media bias at the sentence level, and pinpoint bias sentences that intend to sway readers' opinions. As bias sentences are often expressed in a neutral and factual way, considering broader context outside a sentence can help reveal the bias. In particular, we observe that events in a bias sentence need to be understood in associations with other events in the document. Therefore, we propose to construct an event relation graph to explicitly reason about event-event relations for sentence-level bias iden"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.01722","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/2404.01722/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":"2404.01722","created_at":"2026-07-05T08:03:18.769857+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.01722v1","created_at":"2026-07-05T08:03:18.769857+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.01722","created_at":"2026-07-05T08:03:18.769857+00:00"},{"alias_kind":"pith_short_12","alias_value":"HLNBP4BZPKZ6","created_at":"2026-07-05T08:03:18.769857+00:00"},{"alias_kind":"pith_short_16","alias_value":"HLNBP4BZPKZ6KH4Q","created_at":"2026-07-05T08:03:18.769857+00:00"},{"alias_kind":"pith_short_8","alias_value":"HLNBP4BZ","created_at":"2026-07-05T08:03:18.769857+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.11081","citing_title":"The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HLNBP4BZPKZ6KH4QCBHXPJNJZ7","json":"https://pith.science/pith/HLNBP4BZPKZ6KH4QCBHXPJNJZ7.json","graph_json":"https://pith.science/api/pith-number/HLNBP4BZPKZ6KH4QCBHXPJNJZ7/graph.json","events_json":"https://pith.science/api/pith-number/HLNBP4BZPKZ6KH4QCBHXPJNJZ7/events.json","paper":"https://pith.science/paper/HLNBP4BZ"},"agent_actions":{"view_html":"https://pith.science/pith/HLNBP4BZPKZ6KH4QCBHXPJNJZ7","download_json":"https://pith.science/pith/HLNBP4BZPKZ6KH4QCBHXPJNJZ7.json","view_paper":"https://pith.science/paper/HLNBP4BZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.01722&json=true","fetch_graph":"https://pith.science/api/pith-number/HLNBP4BZPKZ6KH4QCBHXPJNJZ7/graph.json","fetch_events":"https://pith.science/api/pith-number/HLNBP4BZPKZ6KH4QCBHXPJNJZ7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HLNBP4BZPKZ6KH4QCBHXPJNJZ7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HLNBP4BZPKZ6KH4QCBHXPJNJZ7/action/storage_attestation","attest_author":"https://pith.science/pith/HLNBP4BZPKZ6KH4QCBHXPJNJZ7/action/author_attestation","sign_citation":"https://pith.science/pith/HLNBP4BZPKZ6KH4QCBHXPJNJZ7/action/citation_signature","submit_replication":"https://pith.science/pith/HLNBP4BZPKZ6KH4QCBHXPJNJZ7/action/replication_record"}},"created_at":"2026-07-05T08:03:18.769857+00:00","updated_at":"2026-07-05T08:03:18.769857+00:00"}