{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:4BTHDTWNK22V5PCLTCMRL6XGTN","short_pith_number":"pith:4BTHDTWN","canonical_record":{"source":{"id":"2110.14780","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-10-27T21:25:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b962705fc122928a731719a6709eabce843a27cf92b99a96a00a1adaf77dde16","abstract_canon_sha256":"812bef2837a3eda185f0f88159ab6e23134147f3dc1c41013bb0f2655ac1dd06"},"schema_version":"1.0"},"canonical_sha256":"e06671cecd56b55ebc4b989915fae69b4385b3a189274932c513ad6049861e52","source":{"kind":"arxiv","id":"2110.14780","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.14780","created_at":"2026-07-05T03:27:37Z"},{"alias_kind":"arxiv_version","alias_value":"2110.14780v2","created_at":"2026-07-05T03:27:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.14780","created_at":"2026-07-05T03:27:37Z"},{"alias_kind":"pith_short_12","alias_value":"4BTHDTWNK22V","created_at":"2026-07-05T03:27:37Z"},{"alias_kind":"pith_short_16","alias_value":"4BTHDTWNK22V5PCL","created_at":"2026-07-05T03:27:37Z"},{"alias_kind":"pith_short_8","alias_value":"4BTHDTWN","created_at":"2026-07-05T03:27:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:4BTHDTWNK22V5PCLTCMRL6XGTN","target":"record","payload":{"canonical_record":{"source":{"id":"2110.14780","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-10-27T21:25:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b962705fc122928a731719a6709eabce843a27cf92b99a96a00a1adaf77dde16","abstract_canon_sha256":"812bef2837a3eda185f0f88159ab6e23134147f3dc1c41013bb0f2655ac1dd06"},"schema_version":"1.0"},"canonical_sha256":"e06671cecd56b55ebc4b989915fae69b4385b3a189274932c513ad6049861e52","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:27:37.383971Z","signature_b64":"sMa4S77EqkYtz/AMFqLqz2FRummN3VJqKyAD6eFtpRsG2rK+LonZvJHx+IgZ1y4C43WluuH9Ym0Dt7N1yl0tDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e06671cecd56b55ebc4b989915fae69b4385b3a189274932c513ad6049861e52","last_reissued_at":"2026-07-05T03:27:37.383529Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:27:37.383529Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.14780","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":"2026-07-05T03:27:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vly1V4huROJU2phhYSh8/lX4JnYdHmRSPU8F6t7k18T+oW87PUTCwlph57WltTBj5UUyvv5bSEnMGVpvmUlDAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:51:38.910604Z"},"content_sha256":"0c213d6991bda5d837c4c940ba8c66a9760f67c979f11566824817b89ac763e9","schema_version":"1.0","event_id":"sha256:0c213d6991bda5d837c4c940ba8c66a9760f67c979f11566824817b89ac763e9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:4BTHDTWNK22V5PCLTCMRL6XGTN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Combining Vagueness Detection with Deep Learning to Identify Fake News","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Benjamin Icard, Ghislain Atemezing, Guillaume Gadek, Paul \\'Egr\\'e, Paul Gu\\'elorget, Souhir Gahbiche, Sylvain Gatepaille","submitted_at":"2021-10-27T21:25:10Z","abstract_excerpt":"In this paper, we combine two independent detection methods for identifying fake news: the algorithm VAGO uses semantic rules combined with NLP techniques to measure vagueness and subjectivity in texts, while the classifier FAKE-CLF relies on Convolutional Neural Network classification and supervised deep learning to classify texts as biased or legitimate. We compare the results of the two methods on four corpora. We find a positive correlation between the vagueness and subjectivity measures obtained by VAGO, and the classification of text as biased by FAKE-CLF. The comparison yields mutual be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.14780","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/2110.14780/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":"2026-07-05T03:27:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mBjnK8PP/Bs1roUu35AvjFwX9v+P76tJEyyqAWuHYmDbhX09SfX5+b4PaH3RlKEEWZ+spbinx60QH8UM+jhpBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:51:38.911579Z"},"content_sha256":"82e7dec010368711f69af2b5684e2b63b092749aef0774033ce5440ee782de78","schema_version":"1.0","event_id":"sha256:82e7dec010368711f69af2b5684e2b63b092749aef0774033ce5440ee782de78"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4BTHDTWNK22V5PCLTCMRL6XGTN/bundle.json","state_url":"https://pith.science/pith/4BTHDTWNK22V5PCLTCMRL6XGTN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4BTHDTWNK22V5PCLTCMRL6XGTN/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":"2026-07-09T06:51:38Z","links":{"resolver":"https://pith.science/pith/4BTHDTWNK22V5PCLTCMRL6XGTN","bundle":"https://pith.science/pith/4BTHDTWNK22V5PCLTCMRL6XGTN/bundle.json","state":"https://pith.science/pith/4BTHDTWNK22V5PCLTCMRL6XGTN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4BTHDTWNK22V5PCLTCMRL6XGTN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:4BTHDTWNK22V5PCLTCMRL6XGTN","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":"812bef2837a3eda185f0f88159ab6e23134147f3dc1c41013bb0f2655ac1dd06","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-10-27T21:25:10Z","title_canon_sha256":"b962705fc122928a731719a6709eabce843a27cf92b99a96a00a1adaf77dde16"},"schema_version":"1.0","source":{"id":"2110.14780","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.14780","created_at":"2026-07-05T03:27:37Z"},{"alias_kind":"arxiv_version","alias_value":"2110.14780v2","created_at":"2026-07-05T03:27:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.14780","created_at":"2026-07-05T03:27:37Z"},{"alias_kind":"pith_short_12","alias_value":"4BTHDTWNK22V","created_at":"2026-07-05T03:27:37Z"},{"alias_kind":"pith_short_16","alias_value":"4BTHDTWNK22V5PCL","created_at":"2026-07-05T03:27:37Z"},{"alias_kind":"pith_short_8","alias_value":"4BTHDTWN","created_at":"2026-07-05T03:27:37Z"}],"graph_snapshots":[{"event_id":"sha256:82e7dec010368711f69af2b5684e2b63b092749aef0774033ce5440ee782de78","target":"graph","created_at":"2026-07-05T03:27:37Z","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/2110.14780/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we combine two independent detection methods for identifying fake news: the algorithm VAGO uses semantic rules combined with NLP techniques to measure vagueness and subjectivity in texts, while the classifier FAKE-CLF relies on Convolutional Neural Network classification and supervised deep learning to classify texts as biased or legitimate. We compare the results of the two methods on four corpora. We find a positive correlation between the vagueness and subjectivity measures obtained by VAGO, and the classification of text as biased by FAKE-CLF. The comparison yields mutual be","authors_text":"Benjamin Icard, Ghislain Atemezing, Guillaume Gadek, Paul \\'Egr\\'e, Paul Gu\\'elorget, Souhir Gahbiche, Sylvain Gatepaille","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-10-27T21:25:10Z","title":"Combining Vagueness Detection with Deep Learning to Identify Fake News"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.14780","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":"sha256:0c213d6991bda5d837c4c940ba8c66a9760f67c979f11566824817b89ac763e9","target":"record","created_at":"2026-07-05T03:27:37Z","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":"812bef2837a3eda185f0f88159ab6e23134147f3dc1c41013bb0f2655ac1dd06","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-10-27T21:25:10Z","title_canon_sha256":"b962705fc122928a731719a6709eabce843a27cf92b99a96a00a1adaf77dde16"},"schema_version":"1.0","source":{"id":"2110.14780","kind":"arxiv","version":2}},"canonical_sha256":"e06671cecd56b55ebc4b989915fae69b4385b3a189274932c513ad6049861e52","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e06671cecd56b55ebc4b989915fae69b4385b3a189274932c513ad6049861e52","first_computed_at":"2026-07-05T03:27:37.383529Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:27:37.383529Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sMa4S77EqkYtz/AMFqLqz2FRummN3VJqKyAD6eFtpRsG2rK+LonZvJHx+IgZ1y4C43WluuH9Ym0Dt7N1yl0tDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:27:37.383971Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.14780","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0c213d6991bda5d837c4c940ba8c66a9760f67c979f11566824817b89ac763e9","sha256:82e7dec010368711f69af2b5684e2b63b092749aef0774033ce5440ee782de78"],"state_sha256":"5fc034d907dcb693fe1f9afd378cc2c2f7de3437c6b65c8e19e57919e7005687"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iuEN2s2XxoOI5ns/CFcrT3S50Ujx82o/abpX+rVZJ1/tnrhzZ3CfgwB0iTu9dmRDS2xrcEerh/bSn1YjSJ9IBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T06:51:38.915366Z","bundle_sha256":"5a116e83b9bdd87f813daff1b4a4fac96010e72972c80a23cd5ba123d4f17d81"}}