{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:H57V5NASRVE6MAWVNIFGF5PMWG","short_pith_number":"pith:H57V5NAS","schema_version":"1.0","canonical_sha256":"3f7f5eb4128d49e602d56a0a62f5ecb1ad653ff1d8b1cba13770c4b3fe4a890e","source":{"kind":"arxiv","id":"2303.07113","version":1},"attestation_state":"computed","paper":{"title":"FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hao Peng, Pengyuan Zhou, Renyu Yang, Tong Li, Yangyang Li, Yingguang Yang, Yong Liao","submitted_at":"2023-03-10T03:10:08Z","abstract_excerpt":"Social bot detection is of paramount importance to the resilience and security of online social platforms. The state-of-the-art detection models are siloed and have largely overlooked a variety of data characteristics from multiple cross-lingual platforms. Meanwhile, the heterogeneity of data distribution and model architecture makes it intricate to devise an efficient cross-platform and cross-model detection framework. In this paper, we propose FedACK, a new federated adversarial contrastive knowledge distillation framework for social bot detection. We devise a GAN-based federated knowledge d"},"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":"2303.07113","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-10T03:10:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b860decdb5f99eea5e6e97512d3bc72ecd8f1fe73279b167b6314ac9d45d7d19","abstract_canon_sha256":"6cf2901aa157f094d83ee90d6984f7c0a8c51eb3f7d76090d615b1075a8584ac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:50:26.911693Z","signature_b64":"hT2bpyNWYiKoTkb6bpqJ+30rRPuVUrorMxS+vNkwxiEqOXiFAs5aCQtmGDbcrRLIETO5bBnTtV2F8+G1G1BtBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f7f5eb4128d49e602d56a0a62f5ecb1ad653ff1d8b1cba13770c4b3fe4a890e","last_reissued_at":"2026-07-05T05:50:26.911341Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:50:26.911341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hao Peng, Pengyuan Zhou, Renyu Yang, Tong Li, Yangyang Li, Yingguang Yang, Yong Liao","submitted_at":"2023-03-10T03:10:08Z","abstract_excerpt":"Social bot detection is of paramount importance to the resilience and security of online social platforms. The state-of-the-art detection models are siloed and have largely overlooked a variety of data characteristics from multiple cross-lingual platforms. Meanwhile, the heterogeneity of data distribution and model architecture makes it intricate to devise an efficient cross-platform and cross-model detection framework. In this paper, we propose FedACK, a new federated adversarial contrastive knowledge distillation framework for social bot detection. We devise a GAN-based federated knowledge d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.07113","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/2303.07113/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":"2303.07113","created_at":"2026-07-05T05:50:26.911398+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.07113v1","created_at":"2026-07-05T05:50:26.911398+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.07113","created_at":"2026-07-05T05:50:26.911398+00:00"},{"alias_kind":"pith_short_12","alias_value":"H57V5NASRVE6","created_at":"2026-07-05T05:50:26.911398+00:00"},{"alias_kind":"pith_short_16","alias_value":"H57V5NASRVE6MAWV","created_at":"2026-07-05T05:50:26.911398+00:00"},{"alias_kind":"pith_short_8","alias_value":"H57V5NAS","created_at":"2026-07-05T05:50:26.911398+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/H57V5NASRVE6MAWVNIFGF5PMWG","json":"https://pith.science/pith/H57V5NASRVE6MAWVNIFGF5PMWG.json","graph_json":"https://pith.science/api/pith-number/H57V5NASRVE6MAWVNIFGF5PMWG/graph.json","events_json":"https://pith.science/api/pith-number/H57V5NASRVE6MAWVNIFGF5PMWG/events.json","paper":"https://pith.science/paper/H57V5NAS"},"agent_actions":{"view_html":"https://pith.science/pith/H57V5NASRVE6MAWVNIFGF5PMWG","download_json":"https://pith.science/pith/H57V5NASRVE6MAWVNIFGF5PMWG.json","view_paper":"https://pith.science/paper/H57V5NAS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.07113&json=true","fetch_graph":"https://pith.science/api/pith-number/H57V5NASRVE6MAWVNIFGF5PMWG/graph.json","fetch_events":"https://pith.science/api/pith-number/H57V5NASRVE6MAWVNIFGF5PMWG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H57V5NASRVE6MAWVNIFGF5PMWG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H57V5NASRVE6MAWVNIFGF5PMWG/action/storage_attestation","attest_author":"https://pith.science/pith/H57V5NASRVE6MAWVNIFGF5PMWG/action/author_attestation","sign_citation":"https://pith.science/pith/H57V5NASRVE6MAWVNIFGF5PMWG/action/citation_signature","submit_replication":"https://pith.science/pith/H57V5NASRVE6MAWVNIFGF5PMWG/action/replication_record"}},"created_at":"2026-07-05T05:50:26.911398+00:00","updated_at":"2026-07-05T05:50:26.911398+00:00"}