{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NEHIFFXEPKRRU6BLORXIEBARRR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8c1cc63c0d46c571ef180324f2ea30c758669cc0bf7a7b37e1864f54e33ac91a","cross_cats_sorted":["cs.AI","cs.CY","cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-23T00:26:32Z","title_canon_sha256":"5f87bb1cc3dc147421701b2b41ff43a33e0d03aea11f57905fb8ca1cd061f985"},"schema_version":"1.0","source":{"id":"2501.13977","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13977","created_at":"2026-07-05T11:11:39Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13977v3","created_at":"2026-07-05T11:11:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13977","created_at":"2026-07-05T11:11:39Z"},{"alias_kind":"pith_short_12","alias_value":"NEHIFFXEPKRR","created_at":"2026-07-05T11:11:39Z"},{"alias_kind":"pith_short_16","alias_value":"NEHIFFXEPKRRU6BL","created_at":"2026-07-05T11:11:39Z"},{"alias_kind":"pith_short_8","alias_value":"NEHIFFXE","created_at":"2026-07-05T11:11:39Z"}],"graph_snapshots":[{"event_id":"sha256:fadfc63d4d85a7827f1fb5ace7f6555edf430ea29c2358f0726b39b7a00d37f6","target":"graph","created_at":"2026-07-05T11:11:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.13977/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Social media platforms utilize Machine Learning (ML) and Artificial Intelligence (AI) powered recommendation algorithms to maximize user engagement, which can result in inadvertent exposure to harmful content. Current moderation efforts, reliant on classifiers trained with extensive human-annotated data, struggle with scalability and adapting to new forms of harm. To address these challenges, we propose a novel re-ranking approach using Large Language Models (LLMs) in zero-shot and few-shot settings. Our method dynamically assesses and re-ranks content sequences, effectively mitigating harmful","authors_text":"Anshuman Chhabra, Claire Jo, Magdalena Wojcieszak, Muhammad Haroon, Rajvardhan Oak","cross_cats":["cs.AI","cs.CY","cs.SI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-23T00:26:32Z","title":"Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13977","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:eeb1ec08cbfd08657f1ab826559e46ea293e0dc5bb4fc0900bc4e2195af97758","target":"record","created_at":"2026-07-05T11:11:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8c1cc63c0d46c571ef180324f2ea30c758669cc0bf7a7b37e1864f54e33ac91a","cross_cats_sorted":["cs.AI","cs.CY","cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-23T00:26:32Z","title_canon_sha256":"5f87bb1cc3dc147421701b2b41ff43a33e0d03aea11f57905fb8ca1cd061f985"},"schema_version":"1.0","source":{"id":"2501.13977","kind":"arxiv","version":3}},"canonical_sha256":"690e8296e47aa31a782b746e8204118c7b7e93b6a9115f74ede3e844321bb6c3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"690e8296e47aa31a782b746e8204118c7b7e93b6a9115f74ede3e844321bb6c3","first_computed_at":"2026-07-05T11:11:39.995874Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:11:39.995874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YDjK5mJUzL+RCmFi3sAdk3idHyPbvExK+PxvIvh07AbvXGCZWkolKFcwqZ37/t+A0Py8aIJCvto8sl+IVtsPAg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:11:39.996368Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.13977","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eeb1ec08cbfd08657f1ab826559e46ea293e0dc5bb4fc0900bc4e2195af97758","sha256:fadfc63d4d85a7827f1fb5ace7f6555edf430ea29c2358f0726b39b7a00d37f6"],"state_sha256":"df75c5400c94c3eb9c4878f50b7c43bf88ec6edcb9cc7a093acc2570044baa4f"}