{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WO7BIYXRAKTSDJV2SEL2ZB3SRT","short_pith_number":"pith:WO7BIYXR","schema_version":"1.0","canonical_sha256":"b3be1462f102a721a6ba9117ac87728cf618297753fd1180e5f1c12b99908282","source":{"kind":"arxiv","id":"2605.24164","version":1},"attestation_state":"computed","paper":{"title":"CUNY at CLPsych 2026: A Pipeline Approach to Classification and Summarization of Mental Health Changes","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alla Rozovskaya, Amirmohammad Ziaei Bideh, Ava Yahyapour, Shameed Charlomar Job","submitted_at":"2026-05-22T19:35:30Z","abstract_excerpt":"We describe our submission to the CLPsych~2026 Shared Task on capturing and characterizing mental health changes through social media timeline dynamics. To infer the dominant self-states in posts (Tasks 1.1 and 1.2), we ensemble in-context learning of three open-weight large language models using majority voting. For predicting moments of change in a timeline (Task~2), we train supervised classifiers on features derived from Task~1.1 predictions. To summarize the patterns of mood dynamics and their progression over time within a timeline (Task 3.1), we augment in-context example labels predict"},"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":"2605.24164","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-22T19:35:30Z","cross_cats_sorted":[],"title_canon_sha256":"03b8e5322d7244b68c6f354d3397914871d998963b4ab109a94e2885b63ac364","abstract_canon_sha256":"ead1c0ba47258853d1c98958086d98fdf622cab17d94b3bb8f663f473ecaa05f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:02:50.073879Z","signature_b64":"ez0X1gizwLRz6lCTSLm1dXEiJi8a4Nj1oJhQx5DaTjI2uQOG6P+ZeAYXcDvJI6upiuXIOpEfTqwbT/o5/BiaAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3be1462f102a721a6ba9117ac87728cf618297753fd1180e5f1c12b99908282","last_reissued_at":"2026-05-26T01:02:50.073075Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:02:50.073075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CUNY at CLPsych 2026: A Pipeline Approach to Classification and Summarization of Mental Health Changes","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alla Rozovskaya, Amirmohammad Ziaei Bideh, Ava Yahyapour, Shameed Charlomar Job","submitted_at":"2026-05-22T19:35:30Z","abstract_excerpt":"We describe our submission to the CLPsych~2026 Shared Task on capturing and characterizing mental health changes through social media timeline dynamics. To infer the dominant self-states in posts (Tasks 1.1 and 1.2), we ensemble in-context learning of three open-weight large language models using majority voting. For predicting moments of change in a timeline (Task~2), we train supervised classifiers on features derived from Task~1.1 predictions. To summarize the patterns of mood dynamics and their progression over time within a timeline (Task 3.1), we augment in-context example labels predict"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24164","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/2605.24164/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":"2605.24164","created_at":"2026-05-26T01:02:50.073218+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.24164v1","created_at":"2026-05-26T01:02:50.073218+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24164","created_at":"2026-05-26T01:02:50.073218+00:00"},{"alias_kind":"pith_short_12","alias_value":"WO7BIYXRAKTS","created_at":"2026-05-26T01:02:50.073218+00:00"},{"alias_kind":"pith_short_16","alias_value":"WO7BIYXRAKTSDJV2","created_at":"2026-05-26T01:02:50.073218+00:00"},{"alias_kind":"pith_short_8","alias_value":"WO7BIYXR","created_at":"2026-05-26T01:02:50.073218+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/WO7BIYXRAKTSDJV2SEL2ZB3SRT","json":"https://pith.science/pith/WO7BIYXRAKTSDJV2SEL2ZB3SRT.json","graph_json":"https://pith.science/api/pith-number/WO7BIYXRAKTSDJV2SEL2ZB3SRT/graph.json","events_json":"https://pith.science/api/pith-number/WO7BIYXRAKTSDJV2SEL2ZB3SRT/events.json","paper":"https://pith.science/paper/WO7BIYXR"},"agent_actions":{"view_html":"https://pith.science/pith/WO7BIYXRAKTSDJV2SEL2ZB3SRT","download_json":"https://pith.science/pith/WO7BIYXRAKTSDJV2SEL2ZB3SRT.json","view_paper":"https://pith.science/paper/WO7BIYXR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.24164&json=true","fetch_graph":"https://pith.science/api/pith-number/WO7BIYXRAKTSDJV2SEL2ZB3SRT/graph.json","fetch_events":"https://pith.science/api/pith-number/WO7BIYXRAKTSDJV2SEL2ZB3SRT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WO7BIYXRAKTSDJV2SEL2ZB3SRT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WO7BIYXRAKTSDJV2SEL2ZB3SRT/action/storage_attestation","attest_author":"https://pith.science/pith/WO7BIYXRAKTSDJV2SEL2ZB3SRT/action/author_attestation","sign_citation":"https://pith.science/pith/WO7BIYXRAKTSDJV2SEL2ZB3SRT/action/citation_signature","submit_replication":"https://pith.science/pith/WO7BIYXRAKTSDJV2SEL2ZB3SRT/action/replication_record"}},"created_at":"2026-05-26T01:02:50.073218+00:00","updated_at":"2026-05-26T01:02:50.073218+00:00"}