{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:LLHUU7MWEGQJG2AQTTR5EHE7GF","short_pith_number":"pith:LLHUU7MW","canonical_record":{"source":{"id":"2111.07819","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-11-15T15:01:55Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"80e7b39bad62d952db1b5a6cd5867819de3ba61b683efe51c50c78d8d0ea59c6","abstract_canon_sha256":"ebc4db86d332b2383bf93c8f94fe37d46f20543fb00b0059255ba82c6ded5d64"},"schema_version":"1.0"},"canonical_sha256":"5acf4a7d9621a09368109ce3d21c9f3164b9ae2e2d32b97612cecce1aef05f5b","source":{"kind":"arxiv","id":"2111.07819","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.07819","created_at":"2026-07-05T07:42:37Z"},{"alias_kind":"arxiv_version","alias_value":"2111.07819v5","created_at":"2026-07-05T07:42:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.07819","created_at":"2026-07-05T07:42:37Z"},{"alias_kind":"pith_short_12","alias_value":"LLHUU7MWEGQJ","created_at":"2026-07-05T07:42:37Z"},{"alias_kind":"pith_short_16","alias_value":"LLHUU7MWEGQJG2AQ","created_at":"2026-07-05T07:42:37Z"},{"alias_kind":"pith_short_8","alias_value":"LLHUU7MW","created_at":"2026-07-05T07:42:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:LLHUU7MWEGQJG2AQTTR5EHE7GF","target":"record","payload":{"canonical_record":{"source":{"id":"2111.07819","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-11-15T15:01:55Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"80e7b39bad62d952db1b5a6cd5867819de3ba61b683efe51c50c78d8d0ea59c6","abstract_canon_sha256":"ebc4db86d332b2383bf93c8f94fe37d46f20543fb00b0059255ba82c6ded5d64"},"schema_version":"1.0"},"canonical_sha256":"5acf4a7d9621a09368109ce3d21c9f3164b9ae2e2d32b97612cecce1aef05f5b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:42:37.287779Z","signature_b64":"Vma9SibgiVT1741PPOOx/xE7sYFzw26b935/tH56sz+B5hpVi0B88KeXWwHZSgXWzJzggM4/gnQCrqh0RV4KDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5acf4a7d9621a09368109ce3d21c9f3164b9ae2e2d32b97612cecce1aef05f5b","last_reissued_at":"2026-07-05T07:42:37.287366Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:42:37.287366Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.07819","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:42:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"10+glam/BT+nM+Iiurigdcjwt3taHm0fSVN+HKGObJagMcR8DiOfd9zRtIX5AEabCFJNFWFj6apLdufAlpnOCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T21:06:36.124006Z"},"content_sha256":"f03ce20719294ee8415ec9eb3f69c7750f965b451c7dcd1c8c7638aa21340c84","schema_version":"1.0","event_id":"sha256:f03ce20719294ee8415ec9eb3f69c7750f965b451c7dcd1c8c7638aa21340c84"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:LLHUU7MWEGQJG2AQTTR5EHE7GF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Bela Gipp, Jan Philip Wahle, Nischal Ashok, Norman Meuschke, Terry Ruas, Tirthankar Ghosal","submitted_at":"2021-11-15T15:01:55Z","abstract_excerpt":"A drastic rise in potentially life-threatening misinformation has been a by-product of the COVID-19 pandemic. Computational support to identify false information within the massive body of data on the topic is crucial to prevent harm. Researchers proposed many methods for flagging online misinformation related to COVID-19. However, these methods predominantly target specific content types (e.g., news) or platforms (e.g., Twitter). The methods' capabilities to generalize were largely unclear so far. We evaluate fifteen Transformer-based models on five COVID-19 misinformation datasets that inclu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.07819","kind":"arxiv","version":5},"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/2111.07819/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":"2026-07-05T07:42:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8bTX3xycCS74u+QMYslQyaGG69UDRfEnugIMSGW7cuNCIYYsAHybCcxo0P1hDNlBvSu1NvYNFc5NamccsFHLCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T21:06:36.124951Z"},"content_sha256":"770c08a0d9c0d5879707892d4f014ec641434ca81d9e798b803e4be334cfd0df","schema_version":"1.0","event_id":"sha256:770c08a0d9c0d5879707892d4f014ec641434ca81d9e798b803e4be334cfd0df"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LLHUU7MWEGQJG2AQTTR5EHE7GF/bundle.json","state_url":"https://pith.science/pith/LLHUU7MWEGQJG2AQTTR5EHE7GF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LLHUU7MWEGQJG2AQTTR5EHE7GF/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":"2026-08-03T21:06:36Z","links":{"resolver":"https://pith.science/pith/LLHUU7MWEGQJG2AQTTR5EHE7GF","bundle":"https://pith.science/pith/LLHUU7MWEGQJG2AQTTR5EHE7GF/bundle.json","state":"https://pith.science/pith/LLHUU7MWEGQJG2AQTTR5EHE7GF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LLHUU7MWEGQJG2AQTTR5EHE7GF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:LLHUU7MWEGQJG2AQTTR5EHE7GF","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":"ebc4db86d332b2383bf93c8f94fe37d46f20543fb00b0059255ba82c6ded5d64","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-11-15T15:01:55Z","title_canon_sha256":"80e7b39bad62d952db1b5a6cd5867819de3ba61b683efe51c50c78d8d0ea59c6"},"schema_version":"1.0","source":{"id":"2111.07819","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.07819","created_at":"2026-07-05T07:42:37Z"},{"alias_kind":"arxiv_version","alias_value":"2111.07819v5","created_at":"2026-07-05T07:42:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.07819","created_at":"2026-07-05T07:42:37Z"},{"alias_kind":"pith_short_12","alias_value":"LLHUU7MWEGQJ","created_at":"2026-07-05T07:42:37Z"},{"alias_kind":"pith_short_16","alias_value":"LLHUU7MWEGQJG2AQ","created_at":"2026-07-05T07:42:37Z"},{"alias_kind":"pith_short_8","alias_value":"LLHUU7MW","created_at":"2026-07-05T07:42:37Z"}],"graph_snapshots":[{"event_id":"sha256:770c08a0d9c0d5879707892d4f014ec641434ca81d9e798b803e4be334cfd0df","target":"graph","created_at":"2026-07-05T07:42:37Z","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/2111.07819/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A drastic rise in potentially life-threatening misinformation has been a by-product of the COVID-19 pandemic. Computational support to identify false information within the massive body of data on the topic is crucial to prevent harm. Researchers proposed many methods for flagging online misinformation related to COVID-19. However, these methods predominantly target specific content types (e.g., news) or platforms (e.g., Twitter). The methods' capabilities to generalize were largely unclear so far. We evaluate fifteen Transformer-based models on five COVID-19 misinformation datasets that inclu","authors_text":"Bela Gipp, Jan Philip Wahle, Nischal Ashok, Norman Meuschke, Terry Ruas, Tirthankar Ghosal","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-11-15T15:01:55Z","title":"Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.07819","kind":"arxiv","version":5},"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:f03ce20719294ee8415ec9eb3f69c7750f965b451c7dcd1c8c7638aa21340c84","target":"record","created_at":"2026-07-05T07:42:37Z","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":"ebc4db86d332b2383bf93c8f94fe37d46f20543fb00b0059255ba82c6ded5d64","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-11-15T15:01:55Z","title_canon_sha256":"80e7b39bad62d952db1b5a6cd5867819de3ba61b683efe51c50c78d8d0ea59c6"},"schema_version":"1.0","source":{"id":"2111.07819","kind":"arxiv","version":5}},"canonical_sha256":"5acf4a7d9621a09368109ce3d21c9f3164b9ae2e2d32b97612cecce1aef05f5b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5acf4a7d9621a09368109ce3d21c9f3164b9ae2e2d32b97612cecce1aef05f5b","first_computed_at":"2026-07-05T07:42:37.287366Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:42:37.287366Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Vma9SibgiVT1741PPOOx/xE7sYFzw26b935/tH56sz+B5hpVi0B88KeXWwHZSgXWzJzggM4/gnQCrqh0RV4KDg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:42:37.287779Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.07819","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f03ce20719294ee8415ec9eb3f69c7750f965b451c7dcd1c8c7638aa21340c84","sha256:770c08a0d9c0d5879707892d4f014ec641434ca81d9e798b803e4be334cfd0df"],"state_sha256":"7e3aecc6aa39f780205498a0b05c6dcdbc7c654a3c8aeb37392b4fb13f783905"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BIVUysS4P14UWDID0NuZvBcBzC4/HnST9SqrPgCQJWYT2EJTBJcc6zT+fxr00JslZuutROa/j7z8WcJEOo4DCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T21:06:36.132956Z","bundle_sha256":"9fdaf959715ce9139b21c3f7646fa03551b658de720b8cbc4e46280f3f5ac58e"}}