{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CTV6YFPDOZO6KLZMKRWSYZIANJ","short_pith_number":"pith:CTV6YFPD","schema_version":"1.0","canonical_sha256":"14ebec15e3765de52f2c546d2c65006a69fe5be34ac4fc681cd5aa529de6f8e5","source":{"kind":"arxiv","id":"2501.14985","version":1},"attestation_state":"computed","paper":{"title":"DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Tarique Anwar, Tommy Yuan, Yusif Ibrahimov","submitted_at":"2025-01-24T23:42:23Z","abstract_excerpt":"In today's interconnected society, social media platforms have become an important part of our lives, where individuals virtually express their thoughts, emotions, and moods. These expressions offer valuable insights into their mental health. This paper explores the use of platforms like Facebook, $\\mathbb{X}$ (formerly Twitter), and Reddit for mental health assessments. We propose a domain knowledge-infused residual attention model called DepressionX for explainable depression severity detection. Existing deep learning models on this problem have shown considerable performance, but they often"},"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":"2501.14985","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-24T23:42:23Z","cross_cats_sorted":[],"title_canon_sha256":"2e30d18c3ab385e76da05a5bccabfceddbf01de3778206b3c26ee3f00fd63c82","abstract_canon_sha256":"623b56bdccd3e781ffbc0153e4af11ea8b6b35c43a4dda75c01b3accae49f00d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:21.699351Z","signature_b64":"VCwKz+R9JA5WtPMUaccgKq5KCnWKyZ3sbx7Tzpj/zfbOjafxBa6uY3RbHEj9sRtjmWZRJtlY4xreUbcjndSCDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14ebec15e3765de52f2c546d2c65006a69fe5be34ac4fc681cd5aa529de6f8e5","last_reissued_at":"2026-07-05T10:05:21.698765Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:21.698765Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Tarique Anwar, Tommy Yuan, Yusif Ibrahimov","submitted_at":"2025-01-24T23:42:23Z","abstract_excerpt":"In today's interconnected society, social media platforms have become an important part of our lives, where individuals virtually express their thoughts, emotions, and moods. These expressions offer valuable insights into their mental health. This paper explores the use of platforms like Facebook, $\\mathbb{X}$ (formerly Twitter), and Reddit for mental health assessments. We propose a domain knowledge-infused residual attention model called DepressionX for explainable depression severity detection. Existing deep learning models on this problem have shown considerable performance, but they often"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14985","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/2501.14985/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":"2501.14985","created_at":"2026-07-05T10:05:21.698832+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.14985v1","created_at":"2026-07-05T10:05:21.698832+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14985","created_at":"2026-07-05T10:05:21.698832+00:00"},{"alias_kind":"pith_short_12","alias_value":"CTV6YFPDOZO6","created_at":"2026-07-05T10:05:21.698832+00:00"},{"alias_kind":"pith_short_16","alias_value":"CTV6YFPDOZO6KLZM","created_at":"2026-07-05T10:05:21.698832+00:00"},{"alias_kind":"pith_short_8","alias_value":"CTV6YFPD","created_at":"2026-07-05T10:05:21.698832+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.12022","citing_title":"AI Models for Depressive Disorder Detection and Diagnosis: A Review","ref_index":79,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CTV6YFPDOZO6KLZMKRWSYZIANJ","json":"https://pith.science/pith/CTV6YFPDOZO6KLZMKRWSYZIANJ.json","graph_json":"https://pith.science/api/pith-number/CTV6YFPDOZO6KLZMKRWSYZIANJ/graph.json","events_json":"https://pith.science/api/pith-number/CTV6YFPDOZO6KLZMKRWSYZIANJ/events.json","paper":"https://pith.science/paper/CTV6YFPD"},"agent_actions":{"view_html":"https://pith.science/pith/CTV6YFPDOZO6KLZMKRWSYZIANJ","download_json":"https://pith.science/pith/CTV6YFPDOZO6KLZMKRWSYZIANJ.json","view_paper":"https://pith.science/paper/CTV6YFPD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.14985&json=true","fetch_graph":"https://pith.science/api/pith-number/CTV6YFPDOZO6KLZMKRWSYZIANJ/graph.json","fetch_events":"https://pith.science/api/pith-number/CTV6YFPDOZO6KLZMKRWSYZIANJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CTV6YFPDOZO6KLZMKRWSYZIANJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CTV6YFPDOZO6KLZMKRWSYZIANJ/action/storage_attestation","attest_author":"https://pith.science/pith/CTV6YFPDOZO6KLZMKRWSYZIANJ/action/author_attestation","sign_citation":"https://pith.science/pith/CTV6YFPDOZO6KLZMKRWSYZIANJ/action/citation_signature","submit_replication":"https://pith.science/pith/CTV6YFPDOZO6KLZMKRWSYZIANJ/action/replication_record"}},"created_at":"2026-07-05T10:05:21.698832+00:00","updated_at":"2026-07-05T10:05:21.698832+00:00"}