{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZQZ2JIAB2JJDE6NMPD5W4QFZKN","short_pith_number":"pith:ZQZ2JIAB","schema_version":"1.0","canonical_sha256":"cc33a4a001d2523279ac78fb6e40b9537bf9cf51c8ff9858da57515b0fe954e1","source":{"kind":"arxiv","id":"2305.12692","version":1},"attestation_state":"computed","paper":{"title":"MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dong Wang, Huimin Zeng, Lanyu Shang, Yang Zhang, Zhenrui Yue","submitted_at":"2023-05-22T04:00:38Z","abstract_excerpt":"With emerging topics (e.g., COVID-19) on social media as a source for the spreading misinformation, overcoming the distributional shifts between the original training domain (i.e., source domain) and such target domains remains a non-trivial task for misinformation detection. This presents an elusive challenge for early-stage misinformation detection, where a good amount of data and annotations from the target domain is not available for training. To address the data scarcity issue, we propose MetaAdapt, a meta learning based approach for domain adaptive few-shot misinformation detection. Meta"},"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":"2305.12692","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-22T04:00:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f6ec5f33e4d8cc5e08a85201a7ecf744a8fe97517e00102c9fbb88da1e962dcb","abstract_canon_sha256":"5bff86c219fb39eb5edd15137f7775f4afa8c9223071756e22d354801f38ce01"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:12:13.244749Z","signature_b64":"JY3/4zYKbRH+1W1IBksIXUPLfMQY+gmQWkzgu+1JoYLF4W+lQqqKWfrbkYA4QlP8YEEoDhF4m1anDZKrzQ55Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc33a4a001d2523279ac78fb6e40b9537bf9cf51c8ff9858da57515b0fe954e1","last_reissued_at":"2026-07-05T06:12:13.244338Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:12:13.244338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dong Wang, Huimin Zeng, Lanyu Shang, Yang Zhang, Zhenrui Yue","submitted_at":"2023-05-22T04:00:38Z","abstract_excerpt":"With emerging topics (e.g., COVID-19) on social media as a source for the spreading misinformation, overcoming the distributional shifts between the original training domain (i.e., source domain) and such target domains remains a non-trivial task for misinformation detection. This presents an elusive challenge for early-stage misinformation detection, where a good amount of data and annotations from the target domain is not available for training. To address the data scarcity issue, we propose MetaAdapt, a meta learning based approach for domain adaptive few-shot misinformation detection. Meta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12692","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/2305.12692/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":"2305.12692","created_at":"2026-07-05T06:12:13.244396+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.12692v1","created_at":"2026-07-05T06:12:13.244396+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12692","created_at":"2026-07-05T06:12:13.244396+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZQZ2JIAB2JJD","created_at":"2026-07-05T06:12:13.244396+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZQZ2JIAB2JJDE6NM","created_at":"2026-07-05T06:12:13.244396+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZQZ2JIAB","created_at":"2026-07-05T06:12:13.244396+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.12877","citing_title":"ReflectFact: Self-Reflective Agents for Improving Comprehension and Reasoning in Multi-Hop Fact Verification","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZQZ2JIAB2JJDE6NMPD5W4QFZKN","json":"https://pith.science/pith/ZQZ2JIAB2JJDE6NMPD5W4QFZKN.json","graph_json":"https://pith.science/api/pith-number/ZQZ2JIAB2JJDE6NMPD5W4QFZKN/graph.json","events_json":"https://pith.science/api/pith-number/ZQZ2JIAB2JJDE6NMPD5W4QFZKN/events.json","paper":"https://pith.science/paper/ZQZ2JIAB"},"agent_actions":{"view_html":"https://pith.science/pith/ZQZ2JIAB2JJDE6NMPD5W4QFZKN","download_json":"https://pith.science/pith/ZQZ2JIAB2JJDE6NMPD5W4QFZKN.json","view_paper":"https://pith.science/paper/ZQZ2JIAB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.12692&json=true","fetch_graph":"https://pith.science/api/pith-number/ZQZ2JIAB2JJDE6NMPD5W4QFZKN/graph.json","fetch_events":"https://pith.science/api/pith-number/ZQZ2JIAB2JJDE6NMPD5W4QFZKN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZQZ2JIAB2JJDE6NMPD5W4QFZKN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZQZ2JIAB2JJDE6NMPD5W4QFZKN/action/storage_attestation","attest_author":"https://pith.science/pith/ZQZ2JIAB2JJDE6NMPD5W4QFZKN/action/author_attestation","sign_citation":"https://pith.science/pith/ZQZ2JIAB2JJDE6NMPD5W4QFZKN/action/citation_signature","submit_replication":"https://pith.science/pith/ZQZ2JIAB2JJDE6NMPD5W4QFZKN/action/replication_record"}},"created_at":"2026-07-05T06:12:13.244396+00:00","updated_at":"2026-07-05T06:12:13.244396+00:00"}