{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LUZPERJXFRM5K6YDEVLB6ZYAYT","short_pith_number":"pith:LUZPERJX","schema_version":"1.0","canonical_sha256":"5d32f245372c59d57b0325561f6700c4fd7beb35afa3c0feb9888cf22935d3c0","source":{"kind":"arxiv","id":"2401.17840","version":1},"attestation_state":"computed","paper":{"title":"Propagation Dynamics of Rumor vs. Non-rumor across Multiple Social Media Platforms Driven by User Characteristics","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Chao Gao, Dongpeng Hou, Shu Yin, Xianghua Li, Zhen Wang","submitted_at":"2024-01-31T13:58:23Z","abstract_excerpt":"Studying information propagation dynamics in social media can elucidate user behaviors and patterns. However, previous research often focuses on single platforms and fails to differentiate between the nuanced roles of source users and other participants in cascades. To address these limitations, we analyze propagation cascades on Twitter and Weibo combined with a crawled dataset of nearly one million users with authentic attributes. Our preliminary findings from multiple platforms robustly indicate that rumors tend to spread more deeply, while non-rumors distribute more broadly. Interestingly,"},"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":"2401.17840","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.SI","submitted_at":"2024-01-31T13:58:23Z","cross_cats_sorted":[],"title_canon_sha256":"722427c370b5896f0c60ff42d65a7bf4d14fd0518de385d5b5acf6481574d346","abstract_canon_sha256":"7bc55bd8276253475794bd93255d45f34cec09736a3c40ec04713757c4a2637e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:39:43.318727Z","signature_b64":"lE2t+SofDfzKmY/VOGDOVe2vXegwWd+EZSx4WFttH3nZEmCA1ZyNYij+DF4LsEq9+6C//cQ2P7MSW5+dQy7PAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d32f245372c59d57b0325561f6700c4fd7beb35afa3c0feb9888cf22935d3c0","last_reissued_at":"2026-07-05T07:39:43.318300Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:39:43.318300Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Propagation Dynamics of Rumor vs. Non-rumor across Multiple Social Media Platforms Driven by User Characteristics","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Chao Gao, Dongpeng Hou, Shu Yin, Xianghua Li, Zhen Wang","submitted_at":"2024-01-31T13:58:23Z","abstract_excerpt":"Studying information propagation dynamics in social media can elucidate user behaviors and patterns. However, previous research often focuses on single platforms and fails to differentiate between the nuanced roles of source users and other participants in cascades. To address these limitations, we analyze propagation cascades on Twitter and Weibo combined with a crawled dataset of nearly one million users with authentic attributes. Our preliminary findings from multiple platforms robustly indicate that rumors tend to spread more deeply, while non-rumors distribute more broadly. Interestingly,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.17840","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/2401.17840/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":"2401.17840","created_at":"2026-07-05T07:39:43.318361+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.17840v1","created_at":"2026-07-05T07:39:43.318361+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.17840","created_at":"2026-07-05T07:39:43.318361+00:00"},{"alias_kind":"pith_short_12","alias_value":"LUZPERJXFRM5","created_at":"2026-07-05T07:39:43.318361+00:00"},{"alias_kind":"pith_short_16","alias_value":"LUZPERJXFRM5K6YD","created_at":"2026-07-05T07:39:43.318361+00:00"},{"alias_kind":"pith_short_8","alias_value":"LUZPERJX","created_at":"2026-07-05T07:39:43.318361+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.15508","citing_title":"Learning Complex Heterogeneous Multimodal Fake News via Social Latent Network Inference","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LUZPERJXFRM5K6YDEVLB6ZYAYT","json":"https://pith.science/pith/LUZPERJXFRM5K6YDEVLB6ZYAYT.json","graph_json":"https://pith.science/api/pith-number/LUZPERJXFRM5K6YDEVLB6ZYAYT/graph.json","events_json":"https://pith.science/api/pith-number/LUZPERJXFRM5K6YDEVLB6ZYAYT/events.json","paper":"https://pith.science/paper/LUZPERJX"},"agent_actions":{"view_html":"https://pith.science/pith/LUZPERJXFRM5K6YDEVLB6ZYAYT","download_json":"https://pith.science/pith/LUZPERJXFRM5K6YDEVLB6ZYAYT.json","view_paper":"https://pith.science/paper/LUZPERJX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.17840&json=true","fetch_graph":"https://pith.science/api/pith-number/LUZPERJXFRM5K6YDEVLB6ZYAYT/graph.json","fetch_events":"https://pith.science/api/pith-number/LUZPERJXFRM5K6YDEVLB6ZYAYT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LUZPERJXFRM5K6YDEVLB6ZYAYT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LUZPERJXFRM5K6YDEVLB6ZYAYT/action/storage_attestation","attest_author":"https://pith.science/pith/LUZPERJXFRM5K6YDEVLB6ZYAYT/action/author_attestation","sign_citation":"https://pith.science/pith/LUZPERJXFRM5K6YDEVLB6ZYAYT/action/citation_signature","submit_replication":"https://pith.science/pith/LUZPERJXFRM5K6YDEVLB6ZYAYT/action/replication_record"}},"created_at":"2026-07-05T07:39:43.318361+00:00","updated_at":"2026-07-05T07:39:43.318361+00:00"}