{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:L3P6MZZH545RLVW6R5NHF44RSD","short_pith_number":"pith:L3P6MZZH","schema_version":"1.0","canonical_sha256":"5edfe66727ef3b15d6de8f5a72f39190e166e8183b4b08e17b1389cf69a7066e","source":{"kind":"arxiv","id":"2504.00027","version":4},"attestation_state":"computed","paper":{"title":"Opioid Named Entity Recognition (ONER-2025) from Reddit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Fida Ullah, Grigori Sidorov, Ildar Batyrshin, Muhammad Ahmad, Rita Orji","submitted_at":"2025-03-28T20:51:06Z","abstract_excerpt":"The opioid overdose epidemic remains a critical public health crisis, particularly in the United States, leading to significant mortality and societal costs. Social media platforms like Reddit provide vast amounts of unstructured data that offer insights into public perceptions, discussions, and experiences related to opioid use. This study leverages Natural Language Processing (NLP), specifically Opioid Named Entity Recognition (ONER-2025), to extract actionable information from these platforms. Our research makes four key contributions. First, we created a unique, manually annotated dataset "},"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":"2504.00027","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-28T20:51:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"414f6d3b59736775c9079101dad662efe2f7845957767b5536a05017ca7da549","abstract_canon_sha256":"e73603ab28e88c64eab3831973dbb4b83f8e70d27fef9fb1180ec8e691e808a4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:19.265506Z","signature_b64":"80JHSiNx/CKVdMS7a6UfWe9yqPC9I1n2r3hagLS8Mlr9ZDsjsTU5+KPe0fxxpGBzIP3bUi72msZKYbCSDBnvCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5edfe66727ef3b15d6de8f5a72f39190e166e8183b4b08e17b1389cf69a7066e","last_reissued_at":"2026-07-05T11:52:19.264962Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:19.264962Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Opioid Named Entity Recognition (ONER-2025) from Reddit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Fida Ullah, Grigori Sidorov, Ildar Batyrshin, Muhammad Ahmad, Rita Orji","submitted_at":"2025-03-28T20:51:06Z","abstract_excerpt":"The opioid overdose epidemic remains a critical public health crisis, particularly in the United States, leading to significant mortality and societal costs. Social media platforms like Reddit provide vast amounts of unstructured data that offer insights into public perceptions, discussions, and experiences related to opioid use. This study leverages Natural Language Processing (NLP), specifically Opioid Named Entity Recognition (ONER-2025), to extract actionable information from these platforms. Our research makes four key contributions. First, we created a unique, manually annotated dataset "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.00027","kind":"arxiv","version":4},"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/2504.00027/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":"2504.00027","created_at":"2026-07-05T11:52:19.265021+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.00027v4","created_at":"2026-07-05T11:52:19.265021+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.00027","created_at":"2026-07-05T11:52:19.265021+00:00"},{"alias_kind":"pith_short_12","alias_value":"L3P6MZZH545R","created_at":"2026-07-05T11:52:19.265021+00:00"},{"alias_kind":"pith_short_16","alias_value":"L3P6MZZH545RLVW6","created_at":"2026-07-05T11:52:19.265021+00:00"},{"alias_kind":"pith_short_8","alias_value":"L3P6MZZH","created_at":"2026-07-05T11:52:19.265021+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.19467","citing_title":"Inference Gap in Domain Expertise and Machine Intelligence in Named Entity Recognition: Creation of and Insights from a Substance Use-related Dataset","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L3P6MZZH545RLVW6R5NHF44RSD","json":"https://pith.science/pith/L3P6MZZH545RLVW6R5NHF44RSD.json","graph_json":"https://pith.science/api/pith-number/L3P6MZZH545RLVW6R5NHF44RSD/graph.json","events_json":"https://pith.science/api/pith-number/L3P6MZZH545RLVW6R5NHF44RSD/events.json","paper":"https://pith.science/paper/L3P6MZZH"},"agent_actions":{"view_html":"https://pith.science/pith/L3P6MZZH545RLVW6R5NHF44RSD","download_json":"https://pith.science/pith/L3P6MZZH545RLVW6R5NHF44RSD.json","view_paper":"https://pith.science/paper/L3P6MZZH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.00027&json=true","fetch_graph":"https://pith.science/api/pith-number/L3P6MZZH545RLVW6R5NHF44RSD/graph.json","fetch_events":"https://pith.science/api/pith-number/L3P6MZZH545RLVW6R5NHF44RSD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L3P6MZZH545RLVW6R5NHF44RSD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L3P6MZZH545RLVW6R5NHF44RSD/action/storage_attestation","attest_author":"https://pith.science/pith/L3P6MZZH545RLVW6R5NHF44RSD/action/author_attestation","sign_citation":"https://pith.science/pith/L3P6MZZH545RLVW6R5NHF44RSD/action/citation_signature","submit_replication":"https://pith.science/pith/L3P6MZZH545RLVW6R5NHF44RSD/action/replication_record"}},"created_at":"2026-07-05T11:52:19.265021+00:00","updated_at":"2026-07-05T11:52:19.265021+00:00"}