{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:IFLYYPOZU2FVXKG6HRJC7MD4HG","short_pith_number":"pith:IFLYYPOZ","schema_version":"1.0","canonical_sha256":"41578c3dd9a68b5ba8de3c522fb07c398172bb717fac79a321504eb2af1f796b","source":{"kind":"arxiv","id":"2001.05658","version":2},"attestation_state":"computed","paper":{"title":"Uncovering Coordinated Networks on Social Media: Methods and Case Studies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Alessandro Flammini, Bao Tran Truong, Christopher Torres-Lugo, Diogo Pacheco, Filippo Menczer, Pik-Mai Hui","submitted_at":"2020-01-16T05:35:30Z","abstract_excerpt":"Coordinated campaigns are used to influence and manipulate social media platforms and their users, a critical challenge to the free exchange of information online. Here we introduce a general, unsupervised network-based methodology to uncover groups of accounts that are likely coordinated. The proposed method constructs coordination networks based on arbitrary behavioral traces shared among accounts. We present five case studies of influence campaigns, four of which in the diverse contexts of U.S. elections, Hong Kong protests, the Syrian civil war, and cryptocurrency manipulation. In each of "},"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":"2001.05658","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2020-01-16T05:35:30Z","cross_cats_sorted":["physics.soc-ph"],"title_canon_sha256":"498932ffea542cfe81f35e6a2a18f3b353c9fcc60272965e9a8bb4ac31f406dc","abstract_canon_sha256":"b78fc74c1e95684d43e9d2d7f3a55391d4ae416f8e7b2c8a1eafdc1a467acc15"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:30:11.087367Z","signature_b64":"ZIrv9kmkBDuSBy4XnbQ4Mo8B3us9qQ/QplQax1u86J9s6WrwdeZrktGyCpHCfxPxG+Lq66KTjdFidZEW13GTAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41578c3dd9a68b5ba8de3c522fb07c398172bb717fac79a321504eb2af1f796b","last_reissued_at":"2026-07-05T02:30:11.086840Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:30:11.086840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Uncovering Coordinated Networks on Social Media: Methods and Case Studies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Alessandro Flammini, Bao Tran Truong, Christopher Torres-Lugo, Diogo Pacheco, Filippo Menczer, Pik-Mai Hui","submitted_at":"2020-01-16T05:35:30Z","abstract_excerpt":"Coordinated campaigns are used to influence and manipulate social media platforms and their users, a critical challenge to the free exchange of information online. Here we introduce a general, unsupervised network-based methodology to uncover groups of accounts that are likely coordinated. The proposed method constructs coordination networks based on arbitrary behavioral traces shared among accounts. We present five case studies of influence campaigns, four of which in the diverse contexts of U.S. elections, Hong Kong protests, the Syrian civil war, and cryptocurrency manipulation. In each of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.05658","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/2001.05658/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":"2001.05658","created_at":"2026-07-05T02:30:11.086902+00:00"},{"alias_kind":"arxiv_version","alias_value":"2001.05658v2","created_at":"2026-07-05T02:30:11.086902+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.05658","created_at":"2026-07-05T02:30:11.086902+00:00"},{"alias_kind":"pith_short_12","alias_value":"IFLYYPOZU2FV","created_at":"2026-07-05T02:30:11.086902+00:00"},{"alias_kind":"pith_short_16","alias_value":"IFLYYPOZU2FVXKG6","created_at":"2026-07-05T02:30:11.086902+00:00"},{"alias_kind":"pith_short_8","alias_value":"IFLYYPOZ","created_at":"2026-07-05T02:30:11.086902+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19647","citing_title":"From 50K to 8.2 Million in 24 Hours: Vozinha's Algorithmic Consecration and the Multilingual Making of World Cup Visibility","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2603.00646","citing_title":"MoltGraph: A Longitudinal Temporal Graph Dataset of Moltbook for Coordinated-Agent Detection","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IFLYYPOZU2FVXKG6HRJC7MD4HG","json":"https://pith.science/pith/IFLYYPOZU2FVXKG6HRJC7MD4HG.json","graph_json":"https://pith.science/api/pith-number/IFLYYPOZU2FVXKG6HRJC7MD4HG/graph.json","events_json":"https://pith.science/api/pith-number/IFLYYPOZU2FVXKG6HRJC7MD4HG/events.json","paper":"https://pith.science/paper/IFLYYPOZ"},"agent_actions":{"view_html":"https://pith.science/pith/IFLYYPOZU2FVXKG6HRJC7MD4HG","download_json":"https://pith.science/pith/IFLYYPOZU2FVXKG6HRJC7MD4HG.json","view_paper":"https://pith.science/paper/IFLYYPOZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2001.05658&json=true","fetch_graph":"https://pith.science/api/pith-number/IFLYYPOZU2FVXKG6HRJC7MD4HG/graph.json","fetch_events":"https://pith.science/api/pith-number/IFLYYPOZU2FVXKG6HRJC7MD4HG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IFLYYPOZU2FVXKG6HRJC7MD4HG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IFLYYPOZU2FVXKG6HRJC7MD4HG/action/storage_attestation","attest_author":"https://pith.science/pith/IFLYYPOZU2FVXKG6HRJC7MD4HG/action/author_attestation","sign_citation":"https://pith.science/pith/IFLYYPOZU2FVXKG6HRJC7MD4HG/action/citation_signature","submit_replication":"https://pith.science/pith/IFLYYPOZU2FVXKG6HRJC7MD4HG/action/replication_record"}},"created_at":"2026-07-05T02:30:11.086902+00:00","updated_at":"2026-07-05T02:30:11.086902+00:00"}