{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:HFT6RFAK5EZWUR7E7GXVHAZARF","short_pith_number":"pith:HFT6RFAK","schema_version":"1.0","canonical_sha256":"3967e8940ae9336a47e4f9af538320897307f282de944cd89dba292cd9bca785","source":{"kind":"arxiv","id":"1804.04109","version":1},"attestation_state":"computed","paper":{"title":"Influence Estimation on Social Media Networks Using Causal Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Danelle C. Shah, Donald B. Rubin, Edward K. Kao, Olga Simek, Steven T. Smith","submitted_at":"2018-04-11T17:28:41Z","abstract_excerpt":"Estimating influence on social media networks is an important practical and theoretical problem, especially because this new medium is widely exploited as a platform for disinformation and propaganda. This paper introduces a novel approach to influence estimation on social media networks and applies it to the real-world problem of characterizing active influence operations on Twitter during the 2017 French presidential elections. The new influence estimation approach attributes impact by accounting for narrative propagation over the network using a network causal inference framework applied to"},"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":"1804.04109","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-04-11T17:28:41Z","cross_cats_sorted":["physics.soc-ph"],"title_canon_sha256":"b0c13f509f516bc3e97cff9a41f3a978d282a1135ef052259ac0930a4ef069a7","abstract_canon_sha256":"1e63b2ec8cbcb30604e83df92be59754cabfbcbfef7dffc4aca275e1b2ac8a29"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:28.836688Z","signature_b64":"JfkRx/253gXpC9YpDEufApSMPoRxmwNaQaBtnufVLjYX3kEq5FHVe18BQyDpM4q6J6YVTzqeKVloaaC826NvCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3967e8940ae9336a47e4f9af538320897307f282de944cd89dba292cd9bca785","last_reissued_at":"2026-05-18T00:06:28.835949Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:28.835949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Influence Estimation on Social Media Networks Using Causal Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Danelle C. Shah, Donald B. Rubin, Edward K. Kao, Olga Simek, Steven T. Smith","submitted_at":"2018-04-11T17:28:41Z","abstract_excerpt":"Estimating influence on social media networks is an important practical and theoretical problem, especially because this new medium is widely exploited as a platform for disinformation and propaganda. This paper introduces a novel approach to influence estimation on social media networks and applies it to the real-world problem of characterizing active influence operations on Twitter during the 2017 French presidential elections. The new influence estimation approach attributes impact by accounting for narrative propagation over the network using a network causal inference framework applied to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.04109","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1804.04109","created_at":"2026-05-18T00:06:28.836064+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.04109v1","created_at":"2026-05-18T00:06:28.836064+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.04109","created_at":"2026-05-18T00:06:28.836064+00:00"},{"alias_kind":"pith_short_12","alias_value":"HFT6RFAK5EZW","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"HFT6RFAK5EZWUR7E","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"HFT6RFAK","created_at":"2026-05-18T12:32:28.185984+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HFT6RFAK5EZWUR7E7GXVHAZARF","json":"https://pith.science/pith/HFT6RFAK5EZWUR7E7GXVHAZARF.json","graph_json":"https://pith.science/api/pith-number/HFT6RFAK5EZWUR7E7GXVHAZARF/graph.json","events_json":"https://pith.science/api/pith-number/HFT6RFAK5EZWUR7E7GXVHAZARF/events.json","paper":"https://pith.science/paper/HFT6RFAK"},"agent_actions":{"view_html":"https://pith.science/pith/HFT6RFAK5EZWUR7E7GXVHAZARF","download_json":"https://pith.science/pith/HFT6RFAK5EZWUR7E7GXVHAZARF.json","view_paper":"https://pith.science/paper/HFT6RFAK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.04109&json=true","fetch_graph":"https://pith.science/api/pith-number/HFT6RFAK5EZWUR7E7GXVHAZARF/graph.json","fetch_events":"https://pith.science/api/pith-number/HFT6RFAK5EZWUR7E7GXVHAZARF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HFT6RFAK5EZWUR7E7GXVHAZARF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HFT6RFAK5EZWUR7E7GXVHAZARF/action/storage_attestation","attest_author":"https://pith.science/pith/HFT6RFAK5EZWUR7E7GXVHAZARF/action/author_attestation","sign_citation":"https://pith.science/pith/HFT6RFAK5EZWUR7E7GXVHAZARF/action/citation_signature","submit_replication":"https://pith.science/pith/HFT6RFAK5EZWUR7E7GXVHAZARF/action/replication_record"}},"created_at":"2026-05-18T00:06:28.836064+00:00","updated_at":"2026-05-18T00:06:28.836064+00:00"}