{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:IRPLHBHFJLW6RP5WZYO542USMN","short_pith_number":"pith:IRPLHBHF","schema_version":"1.0","canonical_sha256":"445eb384e54aede8bfb6ce1dde6a92634d0e8f2f5f9a8dfc7d3ce753dbceee2a","source":{"kind":"arxiv","id":"2108.03067","version":1},"attestation_state":"computed","paper":{"title":"Deriving Disinformation Insights from Geolocalized Twitter Callouts","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Alun Preece, David Rogers, David Tuxworth, Dimosthenis Antypas, Jose Camacho-Collados, Luis Espinosa-Anke","submitted_at":"2021-08-06T11:39:05Z","abstract_excerpt":"This paper demonstrates a two-stage method for deriving insights from social media data relating to disinformation by applying a combination of geospatial classification and embedding-based language modelling across multiple languages. In particular, the analysis in centered on Twitter and disinformation for three European languages: English, French and Spanish. Firstly, Twitter data is classified into European and non-European sets using BERT. Secondly, Word2vec is applied to the classified texts resulting in Eurocentric, non-Eurocentric and global representations of the data for the three ta"},"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":"2108.03067","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-08-06T11:39:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4c72eb66e899346e125db5b5db5e35784d3c4dc95235d2598725068aa62d7148","abstract_canon_sha256":"e725c51dafb7cf829220f4acaad8ee3cd485d9040a335bcdf0f395cd689ee795"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:03:45.447925Z","signature_b64":"tPCIZqFH9a6Hq7X67d7DyUFoBAv4J3vwPQXJPzfq6xMlsje8lK92iSBhe5pgkhcCgrPfrJJOxjl/y93UuF24Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"445eb384e54aede8bfb6ce1dde6a92634d0e8f2f5f9a8dfc7d3ce753dbceee2a","last_reissued_at":"2026-07-05T03:03:45.447555Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:03:45.447555Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deriving Disinformation Insights from Geolocalized Twitter Callouts","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Alun Preece, David Rogers, David Tuxworth, Dimosthenis Antypas, Jose Camacho-Collados, Luis Espinosa-Anke","submitted_at":"2021-08-06T11:39:05Z","abstract_excerpt":"This paper demonstrates a two-stage method for deriving insights from social media data relating to disinformation by applying a combination of geospatial classification and embedding-based language modelling across multiple languages. In particular, the analysis in centered on Twitter and disinformation for three European languages: English, French and Spanish. Firstly, Twitter data is classified into European and non-European sets using BERT. Secondly, Word2vec is applied to the classified texts resulting in Eurocentric, non-Eurocentric and global representations of the data for the three ta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.03067","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/2108.03067/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":"2108.03067","created_at":"2026-07-05T03:03:45.447610+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.03067v1","created_at":"2026-07-05T03:03:45.447610+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.03067","created_at":"2026-07-05T03:03:45.447610+00:00"},{"alias_kind":"pith_short_12","alias_value":"IRPLHBHFJLW6","created_at":"2026-07-05T03:03:45.447610+00:00"},{"alias_kind":"pith_short_16","alias_value":"IRPLHBHFJLW6RP5W","created_at":"2026-07-05T03:03:45.447610+00:00"},{"alias_kind":"pith_short_8","alias_value":"IRPLHBHF","created_at":"2026-07-05T03:03:45.447610+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/IRPLHBHFJLW6RP5WZYO542USMN","json":"https://pith.science/pith/IRPLHBHFJLW6RP5WZYO542USMN.json","graph_json":"https://pith.science/api/pith-number/IRPLHBHFJLW6RP5WZYO542USMN/graph.json","events_json":"https://pith.science/api/pith-number/IRPLHBHFJLW6RP5WZYO542USMN/events.json","paper":"https://pith.science/paper/IRPLHBHF"},"agent_actions":{"view_html":"https://pith.science/pith/IRPLHBHFJLW6RP5WZYO542USMN","download_json":"https://pith.science/pith/IRPLHBHFJLW6RP5WZYO542USMN.json","view_paper":"https://pith.science/paper/IRPLHBHF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.03067&json=true","fetch_graph":"https://pith.science/api/pith-number/IRPLHBHFJLW6RP5WZYO542USMN/graph.json","fetch_events":"https://pith.science/api/pith-number/IRPLHBHFJLW6RP5WZYO542USMN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IRPLHBHFJLW6RP5WZYO542USMN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IRPLHBHFJLW6RP5WZYO542USMN/action/storage_attestation","attest_author":"https://pith.science/pith/IRPLHBHFJLW6RP5WZYO542USMN/action/author_attestation","sign_citation":"https://pith.science/pith/IRPLHBHFJLW6RP5WZYO542USMN/action/citation_signature","submit_replication":"https://pith.science/pith/IRPLHBHFJLW6RP5WZYO542USMN/action/replication_record"}},"created_at":"2026-07-05T03:03:45.447610+00:00","updated_at":"2026-07-05T03:03:45.447610+00:00"}