{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5PTCOWEIFKO4FDEU46M54MKLLB","short_pith_number":"pith:5PTCOWEI","schema_version":"1.0","canonical_sha256":"ebe62758882a9dc28c94e799de314b5840885331b478795dc57b52cb6b8c75ac","source":{"kind":"arxiv","id":"2409.11032","version":3},"attestation_state":"computed","paper":{"title":"Hierarchical Narrative Analysis: Unraveling Perceptions of Generative AI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hiroki Matsumoto, Riona Matsuoka, Ryohei Hisano, Ryoma Kondo, Takahiro Yoshida, Tomohiro Watanabe","submitted_at":"2024-09-17T09:56:12Z","abstract_excerpt":"Written texts reflect an author's perspective, making the thorough analysis of literature a key research method in fields such as the humanities and social sciences. However, conventional text mining techniques like sentiment analysis and topic modeling are limited in their ability to capture the hierarchical narrative structures that reveal deeper argumentative patterns. To address this gap, we propose a method that leverages large language models (LLMs) to extract and organize these structures into a hierarchical framework. We validate this approach by analyzing public opinions on generative"},"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":"2409.11032","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-09-17T09:56:12Z","cross_cats_sorted":[],"title_canon_sha256":"911476000a415384957220e4a15bb6aeaab4e89b022debdf9f9ed2836108a1e4","abstract_canon_sha256":"9bf7a5ac00119946431045fefc0b9db7b411b7c8b1987b0feb7807e6e2c2c6f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:33:37.612623Z","signature_b64":"0f496m3dYn2FDwJXx3vv5pRn2zmGsahScp4rp8aHYRKVox0IwVsCWVT1XXMHHOHq4uyZBLL5eMYYUNsCFr5YAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebe62758882a9dc28c94e799de314b5840885331b478795dc57b52cb6b8c75ac","last_reissued_at":"2026-07-05T09:33:37.612137Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:33:37.612137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical Narrative Analysis: Unraveling Perceptions of Generative AI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hiroki Matsumoto, Riona Matsuoka, Ryohei Hisano, Ryoma Kondo, Takahiro Yoshida, Tomohiro Watanabe","submitted_at":"2024-09-17T09:56:12Z","abstract_excerpt":"Written texts reflect an author's perspective, making the thorough analysis of literature a key research method in fields such as the humanities and social sciences. However, conventional text mining techniques like sentiment analysis and topic modeling are limited in their ability to capture the hierarchical narrative structures that reveal deeper argumentative patterns. To address this gap, we propose a method that leverages large language models (LLMs) to extract and organize these structures into a hierarchical framework. We validate this approach by analyzing public opinions on generative"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.11032","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/2409.11032/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":"2409.11032","created_at":"2026-07-05T09:33:37.612195+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.11032v3","created_at":"2026-07-05T09:33:37.612195+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.11032","created_at":"2026-07-05T09:33:37.612195+00:00"},{"alias_kind":"pith_short_12","alias_value":"5PTCOWEIFKO4","created_at":"2026-07-05T09:33:37.612195+00:00"},{"alias_kind":"pith_short_16","alias_value":"5PTCOWEIFKO4FDEU","created_at":"2026-07-05T09:33:37.612195+00:00"},{"alias_kind":"pith_short_8","alias_value":"5PTCOWEI","created_at":"2026-07-05T09:33:37.612195+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.19515","citing_title":"Leveraging Large Language Models for Institutional Portfolio Management: Persona-Based Ensembles","ref_index":30,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5PTCOWEIFKO4FDEU46M54MKLLB","json":"https://pith.science/pith/5PTCOWEIFKO4FDEU46M54MKLLB.json","graph_json":"https://pith.science/api/pith-number/5PTCOWEIFKO4FDEU46M54MKLLB/graph.json","events_json":"https://pith.science/api/pith-number/5PTCOWEIFKO4FDEU46M54MKLLB/events.json","paper":"https://pith.science/paper/5PTCOWEI"},"agent_actions":{"view_html":"https://pith.science/pith/5PTCOWEIFKO4FDEU46M54MKLLB","download_json":"https://pith.science/pith/5PTCOWEIFKO4FDEU46M54MKLLB.json","view_paper":"https://pith.science/paper/5PTCOWEI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.11032&json=true","fetch_graph":"https://pith.science/api/pith-number/5PTCOWEIFKO4FDEU46M54MKLLB/graph.json","fetch_events":"https://pith.science/api/pith-number/5PTCOWEIFKO4FDEU46M54MKLLB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5PTCOWEIFKO4FDEU46M54MKLLB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5PTCOWEIFKO4FDEU46M54MKLLB/action/storage_attestation","attest_author":"https://pith.science/pith/5PTCOWEIFKO4FDEU46M54MKLLB/action/author_attestation","sign_citation":"https://pith.science/pith/5PTCOWEIFKO4FDEU46M54MKLLB/action/citation_signature","submit_replication":"https://pith.science/pith/5PTCOWEIFKO4FDEU46M54MKLLB/action/replication_record"}},"created_at":"2026-07-05T09:33:37.612195+00:00","updated_at":"2026-07-05T09:33:37.612195+00:00"}