{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:W6HZROZU65UCGRHLE4OUH7BTLQ","short_pith_number":"pith:W6HZROZU","schema_version":"1.0","canonical_sha256":"b78f98bb34f7682344eb271d43fc335c2a73326058be157eff9966d2715f79b2","source":{"kind":"arxiv","id":"2508.05938","version":1},"attestation_state":"computed","paper":{"title":"Prosocial Behavior Detection in Player Game Chat: From Aligning Human-AI Definitions to Efficient Annotation at Scale","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.CL","authors_text":"Animashree Anandkumar, Deshawn Sambrano, Fereshteh Soltani, Min Kim, Penphob (Andrea) Boonyarungsrit, Rafal Kocielnik, R. Michael Alvarez","submitted_at":"2025-08-08T02:04:14Z","abstract_excerpt":"Detecting prosociality in text--communication intended to affirm, support, or improve others' behavior--is a novel and increasingly important challenge for trust and safety systems. Unlike toxic content detection, prosociality lacks well-established definitions and labeled data, requiring new approaches to both annotation and deployment. We present a practical, three-stage pipeline that enables scalable, high-precision prosocial content classification while minimizing human labeling effort and inference costs. First, we identify the best LLM-based labeling strategy using a small seed set of hu"},"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":"2508.05938","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-08T02:04:14Z","cross_cats_sorted":["cs.AI","cs.CY"],"title_canon_sha256":"b712ae55d75649db383b6d1393c4879f0d8a29b8c736deaeeb25b049573860da","abstract_canon_sha256":"39a7e1adab118f86398c0724327d54bbba615343cda4169f1f542517d8a38fd6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:50:45.919651Z","signature_b64":"xHBWXPQ6x9tF9moy1XCkBaUAtb0WTpVI2QollC5sZ3hZroL5ylDTVzJ4VSv8hUdH+Ll8Fv2KmaAt2ra8n7+VBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b78f98bb34f7682344eb271d43fc335c2a73326058be157eff9966d2715f79b2","last_reissued_at":"2026-07-05T11:50:45.919041Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:50:45.919041Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prosocial Behavior Detection in Player Game Chat: From Aligning Human-AI Definitions to Efficient Annotation at Scale","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.CL","authors_text":"Animashree Anandkumar, Deshawn Sambrano, Fereshteh Soltani, Min Kim, Penphob (Andrea) Boonyarungsrit, Rafal Kocielnik, R. Michael Alvarez","submitted_at":"2025-08-08T02:04:14Z","abstract_excerpt":"Detecting prosociality in text--communication intended to affirm, support, or improve others' behavior--is a novel and increasingly important challenge for trust and safety systems. Unlike toxic content detection, prosociality lacks well-established definitions and labeled data, requiring new approaches to both annotation and deployment. We present a practical, three-stage pipeline that enables scalable, high-precision prosocial content classification while minimizing human labeling effort and inference costs. First, we identify the best LLM-based labeling strategy using a small seed set of hu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.05938","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/2508.05938/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":"2508.05938","created_at":"2026-07-05T11:50:45.919106+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.05938v1","created_at":"2026-07-05T11:50:45.919106+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.05938","created_at":"2026-07-05T11:50:45.919106+00:00"},{"alias_kind":"pith_short_12","alias_value":"W6HZROZU65UC","created_at":"2026-07-05T11:50:45.919106+00:00"},{"alias_kind":"pith_short_16","alias_value":"W6HZROZU65UCGRHL","created_at":"2026-07-05T11:50:45.919106+00:00"},{"alias_kind":"pith_short_8","alias_value":"W6HZROZU","created_at":"2026-07-05T11:50:45.919106+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00467","citing_title":"On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W6HZROZU65UCGRHLE4OUH7BTLQ","json":"https://pith.science/pith/W6HZROZU65UCGRHLE4OUH7BTLQ.json","graph_json":"https://pith.science/api/pith-number/W6HZROZU65UCGRHLE4OUH7BTLQ/graph.json","events_json":"https://pith.science/api/pith-number/W6HZROZU65UCGRHLE4OUH7BTLQ/events.json","paper":"https://pith.science/paper/W6HZROZU"},"agent_actions":{"view_html":"https://pith.science/pith/W6HZROZU65UCGRHLE4OUH7BTLQ","download_json":"https://pith.science/pith/W6HZROZU65UCGRHLE4OUH7BTLQ.json","view_paper":"https://pith.science/paper/W6HZROZU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.05938&json=true","fetch_graph":"https://pith.science/api/pith-number/W6HZROZU65UCGRHLE4OUH7BTLQ/graph.json","fetch_events":"https://pith.science/api/pith-number/W6HZROZU65UCGRHLE4OUH7BTLQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W6HZROZU65UCGRHLE4OUH7BTLQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W6HZROZU65UCGRHLE4OUH7BTLQ/action/storage_attestation","attest_author":"https://pith.science/pith/W6HZROZU65UCGRHLE4OUH7BTLQ/action/author_attestation","sign_citation":"https://pith.science/pith/W6HZROZU65UCGRHLE4OUH7BTLQ/action/citation_signature","submit_replication":"https://pith.science/pith/W6HZROZU65UCGRHLE4OUH7BTLQ/action/replication_record"}},"created_at":"2026-07-05T11:50:45.919106+00:00","updated_at":"2026-07-05T11:50:45.919106+00:00"}