{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:YDVZ7L6ZA6SA53HI2IDWZJHCCT","short_pith_number":"pith:YDVZ7L6Z","canonical_record":{"source":{"id":"1907.07033","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-16T14:32:33Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f3749e44f58a1335afbd1fa64569c83e81a26eabf60a7e99535e6e1af0769b89","abstract_canon_sha256":"494c8c6b7ad00ecd57cac6172223d97a56e30e8f1a1ba841a6621a157fafbb0e"},"schema_version":"1.0"},"canonical_sha256":"c0eb9fafd907a40eece8d2076ca4e214f802398e120708aef08f9143b279bea0","source":{"kind":"arxiv","id":"1907.07033","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.07033","created_at":"2026-05-17T23:40:28Z"},{"alias_kind":"arxiv_version","alias_value":"1907.07033v1","created_at":"2026-05-17T23:40:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.07033","created_at":"2026-05-17T23:40:28Z"},{"alias_kind":"pith_short_12","alias_value":"YDVZ7L6ZA6SA","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"YDVZ7L6ZA6SA53HI","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"YDVZ7L6Z","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:YDVZ7L6ZA6SA53HI2IDWZJHCCT","target":"record","payload":{"canonical_record":{"source":{"id":"1907.07033","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-16T14:32:33Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f3749e44f58a1335afbd1fa64569c83e81a26eabf60a7e99535e6e1af0769b89","abstract_canon_sha256":"494c8c6b7ad00ecd57cac6172223d97a56e30e8f1a1ba841a6621a157fafbb0e"},"schema_version":"1.0"},"canonical_sha256":"c0eb9fafd907a40eece8d2076ca4e214f802398e120708aef08f9143b279bea0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:28.303000Z","signature_b64":"ry/uPF8PkvARYLjruy+Kq4D9zmGq105/tW7X0BYjzUYAyfmOQU6Ipn5Shr8OOaAPLQfSxesto0hSO6J2q2RLBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c0eb9fafd907a40eece8d2076ca4e214f802398e120708aef08f9143b279bea0","last_reissued_at":"2026-05-17T23:40:28.302426Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:28.302426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.07033","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":"2026-05-17T23:40:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eH3nd3j9i/w4BYiVdMcsvgJ6WW2QbjoOb9beC9NbIOCiQuOoZjpP37AVX/vssS+bUPwZrc48Ew0rUITmayLjBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T03:49:31.564151Z"},"content_sha256":"d3c6511551a9dd4fcfc50246205b1984c87d22102c7aa5c15ea4342bef59fb52","schema_version":"1.0","event_id":"sha256:d3c6511551a9dd4fcfc50246205b1984c87d22102c7aa5c15ea4342bef59fb52"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:YDVZ7L6ZA6SA53HI2IDWZJHCCT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Fabio Ciravegna, Jie Gao, Sooji Han","submitted_at":"2019-07-16T14:32:33Z","abstract_excerpt":"The scarcity and class imbalance of training data are known issues in current rumor detection tasks. We propose a straight-forward and general-purpose data augmentation technique which is beneficial to early rumor detection relying on event propagation patterns. The key idea is to exploit massive unlabeled event data sets on social media to augment limited labeled rumor source tweets. This work is based on rumor spreading patterns revealed by recent rumor studies and semantic relatedness between labeled and unlabeled data. A state-of-the-art neural language model (NLM) and large credibility-fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.07033","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":"2026-05-17T23:40:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8RHh59lVEzFWF4uC9nw0RVPk86D4MWDoX6Tevtgd+s9ZGMqcb7BXHnBUQrVkl85QgQdXLr037PlRPGE3ox8pCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T03:49:31.564517Z"},"content_sha256":"c92d350b4d9bda69379c0304e348c823b4a584f2556b904a2e027c2c4921de70","schema_version":"1.0","event_id":"sha256:c92d350b4d9bda69379c0304e348c823b4a584f2556b904a2e027c2c4921de70"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YDVZ7L6ZA6SA53HI2IDWZJHCCT/bundle.json","state_url":"https://pith.science/pith/YDVZ7L6ZA6SA53HI2IDWZJHCCT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YDVZ7L6ZA6SA53HI2IDWZJHCCT/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":"2026-06-04T03:49:31Z","links":{"resolver":"https://pith.science/pith/YDVZ7L6ZA6SA53HI2IDWZJHCCT","bundle":"https://pith.science/pith/YDVZ7L6ZA6SA53HI2IDWZJHCCT/bundle.json","state":"https://pith.science/pith/YDVZ7L6ZA6SA53HI2IDWZJHCCT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YDVZ7L6ZA6SA53HI2IDWZJHCCT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:YDVZ7L6ZA6SA53HI2IDWZJHCCT","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":"494c8c6b7ad00ecd57cac6172223d97a56e30e8f1a1ba841a6621a157fafbb0e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-16T14:32:33Z","title_canon_sha256":"f3749e44f58a1335afbd1fa64569c83e81a26eabf60a7e99535e6e1af0769b89"},"schema_version":"1.0","source":{"id":"1907.07033","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.07033","created_at":"2026-05-17T23:40:28Z"},{"alias_kind":"arxiv_version","alias_value":"1907.07033v1","created_at":"2026-05-17T23:40:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.07033","created_at":"2026-05-17T23:40:28Z"},{"alias_kind":"pith_short_12","alias_value":"YDVZ7L6ZA6SA","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"YDVZ7L6ZA6SA53HI","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"YDVZ7L6Z","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:c92d350b4d9bda69379c0304e348c823b4a584f2556b904a2e027c2c4921de70","target":"graph","created_at":"2026-05-17T23:40:28Z","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":"The scarcity and class imbalance of training data are known issues in current rumor detection tasks. We propose a straight-forward and general-purpose data augmentation technique which is beneficial to early rumor detection relying on event propagation patterns. The key idea is to exploit massive unlabeled event data sets on social media to augment limited labeled rumor source tweets. This work is based on rumor spreading patterns revealed by recent rumor studies and semantic relatedness between labeled and unlabeled data. A state-of-the-art neural language model (NLM) and large credibility-fo","authors_text":"Fabio Ciravegna, Jie Gao, Sooji Han","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-16T14:32:33Z","title":"Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.07033","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:d3c6511551a9dd4fcfc50246205b1984c87d22102c7aa5c15ea4342bef59fb52","target":"record","created_at":"2026-05-17T23:40:28Z","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":"494c8c6b7ad00ecd57cac6172223d97a56e30e8f1a1ba841a6621a157fafbb0e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-16T14:32:33Z","title_canon_sha256":"f3749e44f58a1335afbd1fa64569c83e81a26eabf60a7e99535e6e1af0769b89"},"schema_version":"1.0","source":{"id":"1907.07033","kind":"arxiv","version":1}},"canonical_sha256":"c0eb9fafd907a40eece8d2076ca4e214f802398e120708aef08f9143b279bea0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c0eb9fafd907a40eece8d2076ca4e214f802398e120708aef08f9143b279bea0","first_computed_at":"2026-05-17T23:40:28.302426Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:40:28.302426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ry/uPF8PkvARYLjruy+Kq4D9zmGq105/tW7X0BYjzUYAyfmOQU6Ipn5Shr8OOaAPLQfSxesto0hSO6J2q2RLBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:40:28.303000Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.07033","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d3c6511551a9dd4fcfc50246205b1984c87d22102c7aa5c15ea4342bef59fb52","sha256:c92d350b4d9bda69379c0304e348c823b4a584f2556b904a2e027c2c4921de70"],"state_sha256":"31a7e4e7d3f8b4581eca9a8b20995766ebe838b2a36863da054066f68e36ce1a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ob9v88UaXufqcV5QHR8XRGJd1l/BWGnah6L0zgxMKwIBz4uzI8tlWhNFj7OnqFha/VjT+uMMlLcigRI1HPmeAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T03:49:31.566606Z","bundle_sha256":"c6f58bd0768e5e667b4b8a82498aa66e4900ef88ba7bf120c001e901810bec18"}}