{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:NPXI5AWYI2D6N6G3L7FO3YIHV4","short_pith_number":"pith:NPXI5AWY","schema_version":"1.0","canonical_sha256":"6bee8e82d84687e6f8db5fcaede107af315b1117c62a301d0b7669250abc70de","source":{"kind":"arxiv","id":"2301.00604","version":1},"attestation_state":"computed","paper":{"title":"Russia-Ukraine war: Modeling and Clustering the Sentiments Trends of Various Countries","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Faezeh Azizi, Fatemeh Salmani, Hamed Vahdat-Nejad, Hamid-Reza Nili-Sani, Mohammad Ghasem Akbari","submitted_at":"2023-01-02T11:32:47Z","abstract_excerpt":"With Twitter's growth and popularity, a huge number of views are shared by users on various topics, making this platform a valuable information source on various political, social, and economic issues. This paper investigates English tweets on the Russia-Ukraine war to analyze trends reflecting users' opinions and sentiments regarding the conflict. The tweets' positive and negative sentiments are analyzed using a BERT-based model, and the time series associated with the frequency of positive and negative tweets for various countries is calculated. Then, we propose a method based on the neighbo"},"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":"2301.00604","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2023-01-02T11:32:47Z","cross_cats_sorted":[],"title_canon_sha256":"c5086e8d66c9e06b17e433fe4a1031ab554562f9c30419323077d7405c657ddd","abstract_canon_sha256":"e75c01b612b73438a2591f3877add4939cf0d73727c6d3dee815b7ca9fc0e686"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:29:48.192999Z","signature_b64":"KgDQr0yhHKjJP/QnUwrfLeA44LUcQqU8ohSLjKOrl6GqPauVZ5M+UMA7fVGM7GIoK+MGMPiwiAJd4aJFXIE4Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6bee8e82d84687e6f8db5fcaede107af315b1117c62a301d0b7669250abc70de","last_reissued_at":"2026-07-05T05:29:48.192506Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:29:48.192506Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Russia-Ukraine war: Modeling and Clustering the Sentiments Trends of Various Countries","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Faezeh Azizi, Fatemeh Salmani, Hamed Vahdat-Nejad, Hamid-Reza Nili-Sani, Mohammad Ghasem Akbari","submitted_at":"2023-01-02T11:32:47Z","abstract_excerpt":"With Twitter's growth and popularity, a huge number of views are shared by users on various topics, making this platform a valuable information source on various political, social, and economic issues. This paper investigates English tweets on the Russia-Ukraine war to analyze trends reflecting users' opinions and sentiments regarding the conflict. The tweets' positive and negative sentiments are analyzed using a BERT-based model, and the time series associated with the frequency of positive and negative tweets for various countries is calculated. Then, we propose a method based on the neighbo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.00604","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/2301.00604/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":"2301.00604","created_at":"2026-07-05T05:29:48.192565+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.00604v1","created_at":"2026-07-05T05:29:48.192565+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.00604","created_at":"2026-07-05T05:29:48.192565+00:00"},{"alias_kind":"pith_short_12","alias_value":"NPXI5AWYI2D6","created_at":"2026-07-05T05:29:48.192565+00:00"},{"alias_kind":"pith_short_16","alias_value":"NPXI5AWYI2D6N6G3","created_at":"2026-07-05T05:29:48.192565+00:00"},{"alias_kind":"pith_short_8","alias_value":"NPXI5AWY","created_at":"2026-07-05T05:29:48.192565+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/NPXI5AWYI2D6N6G3L7FO3YIHV4","json":"https://pith.science/pith/NPXI5AWYI2D6N6G3L7FO3YIHV4.json","graph_json":"https://pith.science/api/pith-number/NPXI5AWYI2D6N6G3L7FO3YIHV4/graph.json","events_json":"https://pith.science/api/pith-number/NPXI5AWYI2D6N6G3L7FO3YIHV4/events.json","paper":"https://pith.science/paper/NPXI5AWY"},"agent_actions":{"view_html":"https://pith.science/pith/NPXI5AWYI2D6N6G3L7FO3YIHV4","download_json":"https://pith.science/pith/NPXI5AWYI2D6N6G3L7FO3YIHV4.json","view_paper":"https://pith.science/paper/NPXI5AWY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.00604&json=true","fetch_graph":"https://pith.science/api/pith-number/NPXI5AWYI2D6N6G3L7FO3YIHV4/graph.json","fetch_events":"https://pith.science/api/pith-number/NPXI5AWYI2D6N6G3L7FO3YIHV4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NPXI5AWYI2D6N6G3L7FO3YIHV4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NPXI5AWYI2D6N6G3L7FO3YIHV4/action/storage_attestation","attest_author":"https://pith.science/pith/NPXI5AWYI2D6N6G3L7FO3YIHV4/action/author_attestation","sign_citation":"https://pith.science/pith/NPXI5AWYI2D6N6G3L7FO3YIHV4/action/citation_signature","submit_replication":"https://pith.science/pith/NPXI5AWYI2D6N6G3L7FO3YIHV4/action/replication_record"}},"created_at":"2026-07-05T05:29:48.192565+00:00","updated_at":"2026-07-05T05:29:48.192565+00:00"}