{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:RF44PV66IHEPUHTT4ZMZW2ZP6Q","short_pith_number":"pith:RF44PV66","canonical_record":{"source":{"id":"1809.06416","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-09-17T19:51:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"cc72ad4f25ea57505301f7537973fd9b5c719fb31b82df7c8294a93879e281e3","abstract_canon_sha256":"aa49c130fb2a5a6c9fe10de958b7196fafde3578162256284bbe73080002940b"},"schema_version":"1.0"},"canonical_sha256":"8979c7d7de41c8fa1e73e6599b6b2ff40454893fdc20b13ec2b4a16c81384323","source":{"kind":"arxiv","id":"1809.06416","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.06416","created_at":"2026-05-18T00:05:27Z"},{"alias_kind":"arxiv_version","alias_value":"1809.06416v1","created_at":"2026-05-18T00:05:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.06416","created_at":"2026-05-18T00:05:27Z"},{"alias_kind":"pith_short_12","alias_value":"RF44PV66IHEP","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RF44PV66IHEPUHTT","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RF44PV66","created_at":"2026-05-18T12:32:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:RF44PV66IHEPUHTT4ZMZW2ZP6Q","target":"record","payload":{"canonical_record":{"source":{"id":"1809.06416","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-09-17T19:51:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"cc72ad4f25ea57505301f7537973fd9b5c719fb31b82df7c8294a93879e281e3","abstract_canon_sha256":"aa49c130fb2a5a6c9fe10de958b7196fafde3578162256284bbe73080002940b"},"schema_version":"1.0"},"canonical_sha256":"8979c7d7de41c8fa1e73e6599b6b2ff40454893fdc20b13ec2b4a16c81384323","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:27.258546Z","signature_b64":"8G30lvQDWAuiBbK+1a6u2n/ImJzESgqfsII26LkRIS38URmOe8RMNSvT2dIcMeXcx9zWf2jM3P0MJyBJHo78Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8979c7d7de41c8fa1e73e6599b6b2ff40454893fdc20b13ec2b4a16c81384323","last_reissued_at":"2026-05-18T00:05:27.258050Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:27.258050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.06416","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:05:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/yArfkHaRKzGjZiW0ZTXfPYjpyErbppJ7FPtI0KBuy5RyvsuQMhUqFQRuNfIHVtIu1Eji/gF4ODo251R5roYBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T15:22:55.608814Z"},"content_sha256":"4fe985bbc9d96220fbf3692626c87939ed6183a3511ef5e98c014e64a720e69c","schema_version":"1.0","event_id":"sha256:4fe985bbc9d96220fbf3692626c87939ed6183a3511ef5e98c014e64a720e69c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:RF44PV66IHEPUHTT4ZMZW2ZP6Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Andrew Yates, Gerhard Weikum, Kashyap Popat, Subhabrata Mukherjee","submitted_at":"2018-09-17T19:51:18Z","abstract_excerpt":"Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human inte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.06416","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":""},"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":"2026-05-18T00:05:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tlzhbkz6WzGn42xcLxb3v4eswldHjGcYB7efQQLMuwmNIkb3TKPRDPoLA4rY8dXvUF0qScRn/afHsB8oYCJtCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T15:22:55.609184Z"},"content_sha256":"9246e879fd3f7dbea309fc92d48a4f9758d2eec5fe41d27f379cdb5da799087f","schema_version":"1.0","event_id":"sha256:9246e879fd3f7dbea309fc92d48a4f9758d2eec5fe41d27f379cdb5da799087f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RF44PV66IHEPUHTT4ZMZW2ZP6Q/bundle.json","state_url":"https://pith.science/pith/RF44PV66IHEPUHTT4ZMZW2ZP6Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RF44PV66IHEPUHTT4ZMZW2ZP6Q/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":"2026-05-28T15:22:55Z","links":{"resolver":"https://pith.science/pith/RF44PV66IHEPUHTT4ZMZW2ZP6Q","bundle":"https://pith.science/pith/RF44PV66IHEPUHTT4ZMZW2ZP6Q/bundle.json","state":"https://pith.science/pith/RF44PV66IHEPUHTT4ZMZW2ZP6Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RF44PV66IHEPUHTT4ZMZW2ZP6Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:RF44PV66IHEPUHTT4ZMZW2ZP6Q","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":"aa49c130fb2a5a6c9fe10de958b7196fafde3578162256284bbe73080002940b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-09-17T19:51:18Z","title_canon_sha256":"cc72ad4f25ea57505301f7537973fd9b5c719fb31b82df7c8294a93879e281e3"},"schema_version":"1.0","source":{"id":"1809.06416","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.06416","created_at":"2026-05-18T00:05:27Z"},{"alias_kind":"arxiv_version","alias_value":"1809.06416v1","created_at":"2026-05-18T00:05:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.06416","created_at":"2026-05-18T00:05:27Z"},{"alias_kind":"pith_short_12","alias_value":"RF44PV66IHEP","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RF44PV66IHEPUHTT","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RF44PV66","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:9246e879fd3f7dbea309fc92d48a4f9758d2eec5fe41d27f379cdb5da799087f","target":"graph","created_at":"2026-05-18T00:05:27Z","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"},"paper":{"abstract_excerpt":"Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human inte","authors_text":"Andrew Yates, Gerhard Weikum, Kashyap Popat, Subhabrata Mukherjee","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-09-17T19:51:18Z","title":"DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.06416","kind":"arxiv","version":1},"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":"sha256:4fe985bbc9d96220fbf3692626c87939ed6183a3511ef5e98c014e64a720e69c","target":"record","created_at":"2026-05-18T00:05:27Z","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":"aa49c130fb2a5a6c9fe10de958b7196fafde3578162256284bbe73080002940b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-09-17T19:51:18Z","title_canon_sha256":"cc72ad4f25ea57505301f7537973fd9b5c719fb31b82df7c8294a93879e281e3"},"schema_version":"1.0","source":{"id":"1809.06416","kind":"arxiv","version":1}},"canonical_sha256":"8979c7d7de41c8fa1e73e6599b6b2ff40454893fdc20b13ec2b4a16c81384323","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8979c7d7de41c8fa1e73e6599b6b2ff40454893fdc20b13ec2b4a16c81384323","first_computed_at":"2026-05-18T00:05:27.258050Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:27.258050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8G30lvQDWAuiBbK+1a6u2n/ImJzESgqfsII26LkRIS38URmOe8RMNSvT2dIcMeXcx9zWf2jM3P0MJyBJHo78Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:27.258546Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.06416","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4fe985bbc9d96220fbf3692626c87939ed6183a3511ef5e98c014e64a720e69c","sha256:9246e879fd3f7dbea309fc92d48a4f9758d2eec5fe41d27f379cdb5da799087f"],"state_sha256":"eaafdb59728ef2b81be8f0f96597d69ff8f5e6d3d2aef1a188b8c1f7ca247f86"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AjEvYE79QS9qh8g9vlroHS8weEwRhmd5NyU96HEZvnxOYRH5er4FseTfzuksOeRV9xdDflY5rpp3nwrxwPajDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T15:22:55.612127Z","bundle_sha256":"e3a4f72398667f423d23d65573dd578b89c280c194621fe31487fe02deb58155"}}