{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:FDQOTOKVPAJVJOJNVJFMOGKIOO","short_pith_number":"pith:FDQOTOKV","canonical_record":{"source":{"id":"2203.11724","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-20T11:22:59Z","cross_cats_sorted":["cs.AI","cs.SI"],"title_canon_sha256":"1e118716236af0716411118473f4e2f0a038e19d5f9b7e01ce0740c15a177a7d","abstract_canon_sha256":"824695e78bd8a6e5dc745fd7be75cc280bd920b76cfd13a3803480b9f2004b62"},"schema_version":"1.0"},"canonical_sha256":"28e0e9b955781354b92daa4ac719487383638bc9f72368a126aef0ca19c0c355","source":{"kind":"arxiv","id":"2203.11724","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.11724","created_at":"2026-07-05T04:58:54Z"},{"alias_kind":"arxiv_version","alias_value":"2203.11724v2","created_at":"2026-07-05T04:58:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.11724","created_at":"2026-07-05T04:58:54Z"},{"alias_kind":"pith_short_12","alias_value":"FDQOTOKVPAJV","created_at":"2026-07-05T04:58:54Z"},{"alias_kind":"pith_short_16","alias_value":"FDQOTOKVPAJVJOJN","created_at":"2026-07-05T04:58:54Z"},{"alias_kind":"pith_short_8","alias_value":"FDQOTOKV","created_at":"2026-07-05T04:58:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:FDQOTOKVPAJVJOJNVJFMOGKIOO","target":"record","payload":{"canonical_record":{"source":{"id":"2203.11724","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-20T11:22:59Z","cross_cats_sorted":["cs.AI","cs.SI"],"title_canon_sha256":"1e118716236af0716411118473f4e2f0a038e19d5f9b7e01ce0740c15a177a7d","abstract_canon_sha256":"824695e78bd8a6e5dc745fd7be75cc280bd920b76cfd13a3803480b9f2004b62"},"schema_version":"1.0"},"canonical_sha256":"28e0e9b955781354b92daa4ac719487383638bc9f72368a126aef0ca19c0c355","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:58:54.503731Z","signature_b64":"4XhZzOv31vZhowsGWDmENAPYIq7vX+Otp8G2L36xhm6/Brca1OWiEjKqnxsh5uRkfFLyET8+FmpElp/pX+h9Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"28e0e9b955781354b92daa4ac719487383638bc9f72368a126aef0ca19c0c355","last_reissued_at":"2026-07-05T04:58:54.503256Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:58:54.503256Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.11724","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:58:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ltnB5jGWJIRM1+LvC8CBPn6y8xO9x1hP0ojHo0gFt2p9ItOvBYF0BZ0uc9fXG8gFuv3TcjLWknp+8INYG6bxDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-15T19:27:59.829767Z"},"content_sha256":"f48b3c046dfcaf2114140195c4490305232f260a52fda288e0554829c0d90806","schema_version":"1.0","event_id":"sha256:f48b3c046dfcaf2114140195c4490305232f260a52fda288e0554829c0d90806"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:FDQOTOKVPAJVJOJNVJFMOGKIOO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Explainable Misinformation Detection Across Multiple Social Media Platforms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SI"],"primary_cat":"cs.LG","authors_text":"Ajith Abraham, Ananya Srivastava, Bhargav Yagnik, Gargi Joshi, Ketan Kotecha, Lubna A Gabralla, Mohammed Hasan, Rahee Walambe, Zainuddin Saiyed","submitted_at":"2022-03-20T11:22:59Z","abstract_excerpt":"In this work, the integration of two machine learning approaches, namely domain adaptation and explainable AI, is proposed to address these two issues of generalized detection and explainability. Firstly the Domain Adversarial Neural Network (DANN) develops a generalized misinformation detector across multiple social media platforms DANN is employed to generate the classification results for test domains with relevant but unseen data. The DANN-based model, a traditional black-box model, cannot justify its outcome, i.e., the labels for the target domain. Hence a Local Interpretable Model-Agnost"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.11724","kind":"arxiv","version":2},"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/2203.11724/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:58:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QnuxrjnCzS5m31+ET4Y7iitMfpRh8QbKit9SiU6yo2qeXvXlEK1XWLr4nq7FAS3g9o3NrxZE8vq1j8V/D5qqBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-15T19:27:59.830146Z"},"content_sha256":"75d352c82ed3c272dc5425db04e821ceae3605dfbf08981a35e690d418133e06","schema_version":"1.0","event_id":"sha256:75d352c82ed3c272dc5425db04e821ceae3605dfbf08981a35e690d418133e06"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FDQOTOKVPAJVJOJNVJFMOGKIOO/bundle.json","state_url":"https://pith.science/pith/FDQOTOKVPAJVJOJNVJFMOGKIOO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FDQOTOKVPAJVJOJNVJFMOGKIOO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-15T19:27:59Z","links":{"resolver":"https://pith.science/pith/FDQOTOKVPAJVJOJNVJFMOGKIOO","bundle":"https://pith.science/pith/FDQOTOKVPAJVJOJNVJFMOGKIOO/bundle.json","state":"https://pith.science/pith/FDQOTOKVPAJVJOJNVJFMOGKIOO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FDQOTOKVPAJVJOJNVJFMOGKIOO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:FDQOTOKVPAJVJOJNVJFMOGKIOO","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":"824695e78bd8a6e5dc745fd7be75cc280bd920b76cfd13a3803480b9f2004b62","cross_cats_sorted":["cs.AI","cs.SI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-20T11:22:59Z","title_canon_sha256":"1e118716236af0716411118473f4e2f0a038e19d5f9b7e01ce0740c15a177a7d"},"schema_version":"1.0","source":{"id":"2203.11724","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.11724","created_at":"2026-07-05T04:58:54Z"},{"alias_kind":"arxiv_version","alias_value":"2203.11724v2","created_at":"2026-07-05T04:58:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.11724","created_at":"2026-07-05T04:58:54Z"},{"alias_kind":"pith_short_12","alias_value":"FDQOTOKVPAJV","created_at":"2026-07-05T04:58:54Z"},{"alias_kind":"pith_short_16","alias_value":"FDQOTOKVPAJVJOJN","created_at":"2026-07-05T04:58:54Z"},{"alias_kind":"pith_short_8","alias_value":"FDQOTOKV","created_at":"2026-07-05T04:58:54Z"}],"graph_snapshots":[{"event_id":"sha256:75d352c82ed3c272dc5425db04e821ceae3605dfbf08981a35e690d418133e06","target":"graph","created_at":"2026-07-05T04:58:54Z","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/2203.11724/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, the integration of two machine learning approaches, namely domain adaptation and explainable AI, is proposed to address these two issues of generalized detection and explainability. Firstly the Domain Adversarial Neural Network (DANN) develops a generalized misinformation detector across multiple social media platforms DANN is employed to generate the classification results for test domains with relevant but unseen data. The DANN-based model, a traditional black-box model, cannot justify its outcome, i.e., the labels for the target domain. Hence a Local Interpretable Model-Agnost","authors_text":"Ajith Abraham, Ananya Srivastava, Bhargav Yagnik, Gargi Joshi, Ketan Kotecha, Lubna A Gabralla, Mohammed Hasan, Rahee Walambe, Zainuddin Saiyed","cross_cats":["cs.AI","cs.SI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-20T11:22:59Z","title":"Explainable Misinformation Detection Across Multiple Social Media Platforms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.11724","kind":"arxiv","version":2},"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:f48b3c046dfcaf2114140195c4490305232f260a52fda288e0554829c0d90806","target":"record","created_at":"2026-07-05T04:58:54Z","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":"824695e78bd8a6e5dc745fd7be75cc280bd920b76cfd13a3803480b9f2004b62","cross_cats_sorted":["cs.AI","cs.SI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-20T11:22:59Z","title_canon_sha256":"1e118716236af0716411118473f4e2f0a038e19d5f9b7e01ce0740c15a177a7d"},"schema_version":"1.0","source":{"id":"2203.11724","kind":"arxiv","version":2}},"canonical_sha256":"28e0e9b955781354b92daa4ac719487383638bc9f72368a126aef0ca19c0c355","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"28e0e9b955781354b92daa4ac719487383638bc9f72368a126aef0ca19c0c355","first_computed_at":"2026-07-05T04:58:54.503256Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:58:54.503256Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4XhZzOv31vZhowsGWDmENAPYIq7vX+Otp8G2L36xhm6/Brca1OWiEjKqnxsh5uRkfFLyET8+FmpElp/pX+h9Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T04:58:54.503731Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.11724","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f48b3c046dfcaf2114140195c4490305232f260a52fda288e0554829c0d90806","sha256:75d352c82ed3c272dc5425db04e821ceae3605dfbf08981a35e690d418133e06"],"state_sha256":"c0e2b92d45926aaf370f7e9fbbceecc156170586f2a111b442cf9b2854f545f1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WbzQlWw8VIWokKH9bK8AVi10JRJ/lblppSohXCv0ZDk+mbd4WrJB1kpK/dw1yjU99Xq1/SyvAtIxX9wcl6TWAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-15T19:27:59.832340Z","bundle_sha256":"4fd42e41875ede55ec0cda33afa6cd1c9a9d2bcafa89cdc50e29951eef2d5f93"}}