{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:MPFMSEYFGCQP4TFT44HUF34SYL","short_pith_number":"pith:MPFMSEYF","schema_version":"1.0","canonical_sha256":"63cac9130530a0fe4cb3e70f42ef92c2d1effff3a129bd235d0bd2fd35d726c3","source":{"kind":"arxiv","id":"2105.03313","version":1},"attestation_state":"computed","paper":{"title":"Looking for COVID-19 misinformation in multilingual social media texts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.CL","authors_text":"Ambesh Shekhar, Genoveva Vargas-Solar, Mehrdad Farokhnejad, Raj Ratn Pranesh","submitted_at":"2021-05-03T14:30:49Z","abstract_excerpt":"This paper presents the Multilingual COVID-19 Analysis Method (CMTA) for detecting and observing the spread of misinformation about this disease within texts. CMTA proposes a data science (DS) pipeline that applies machine learning models for processing, classifying (Dense-CNN) and analyzing (MBERT) multilingual (micro)-texts. DS pipeline data preparation tasks extract features from multilingual textual data and categorize it into specific information classes (i.e., 'false', 'partly false', 'misleading'). The CMTA pipeline has been experimented with multilingual micro-texts (tweets), showing m"},"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":"2105.03313","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-05-03T14:30:49Z","cross_cats_sorted":["cs.DB"],"title_canon_sha256":"a2f68f8a8cc0f614e406b98fef6bd2952cc68e59f390a8da2651c019fb2b74e0","abstract_canon_sha256":"ac756d3b22068747e0c697559a7d8092f40afdee97f8284410db14483e21d979"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:38:24.239716Z","signature_b64":"v7RZrZAnos9wADM3ENvslK3PLa8GD/L1QnC5IseYEIzIow/tGQIVV6RR0owsD8hkRLZoMKJx7tonl32LInRMAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63cac9130530a0fe4cb3e70f42ef92c2d1effff3a129bd235d0bd2fd35d726c3","last_reissued_at":"2026-07-05T02:38:24.239335Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:38:24.239335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Looking for COVID-19 misinformation in multilingual social media texts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.CL","authors_text":"Ambesh Shekhar, Genoveva Vargas-Solar, Mehrdad Farokhnejad, Raj Ratn Pranesh","submitted_at":"2021-05-03T14:30:49Z","abstract_excerpt":"This paper presents the Multilingual COVID-19 Analysis Method (CMTA) for detecting and observing the spread of misinformation about this disease within texts. CMTA proposes a data science (DS) pipeline that applies machine learning models for processing, classifying (Dense-CNN) and analyzing (MBERT) multilingual (micro)-texts. DS pipeline data preparation tasks extract features from multilingual textual data and categorize it into specific information classes (i.e., 'false', 'partly false', 'misleading'). The CMTA pipeline has been experimented with multilingual micro-texts (tweets), showing m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.03313","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/2105.03313/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":"2105.03313","created_at":"2026-07-05T02:38:24.239391+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.03313v1","created_at":"2026-07-05T02:38:24.239391+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.03313","created_at":"2026-07-05T02:38:24.239391+00:00"},{"alias_kind":"pith_short_12","alias_value":"MPFMSEYFGCQP","created_at":"2026-07-05T02:38:24.239391+00:00"},{"alias_kind":"pith_short_16","alias_value":"MPFMSEYFGCQP4TFT","created_at":"2026-07-05T02:38:24.239391+00:00"},{"alias_kind":"pith_short_8","alias_value":"MPFMSEYF","created_at":"2026-07-05T02:38:24.239391+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/MPFMSEYFGCQP4TFT44HUF34SYL","json":"https://pith.science/pith/MPFMSEYFGCQP4TFT44HUF34SYL.json","graph_json":"https://pith.science/api/pith-number/MPFMSEYFGCQP4TFT44HUF34SYL/graph.json","events_json":"https://pith.science/api/pith-number/MPFMSEYFGCQP4TFT44HUF34SYL/events.json","paper":"https://pith.science/paper/MPFMSEYF"},"agent_actions":{"view_html":"https://pith.science/pith/MPFMSEYFGCQP4TFT44HUF34SYL","download_json":"https://pith.science/pith/MPFMSEYFGCQP4TFT44HUF34SYL.json","view_paper":"https://pith.science/paper/MPFMSEYF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.03313&json=true","fetch_graph":"https://pith.science/api/pith-number/MPFMSEYFGCQP4TFT44HUF34SYL/graph.json","fetch_events":"https://pith.science/api/pith-number/MPFMSEYFGCQP4TFT44HUF34SYL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MPFMSEYFGCQP4TFT44HUF34SYL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MPFMSEYFGCQP4TFT44HUF34SYL/action/storage_attestation","attest_author":"https://pith.science/pith/MPFMSEYFGCQP4TFT44HUF34SYL/action/author_attestation","sign_citation":"https://pith.science/pith/MPFMSEYFGCQP4TFT44HUF34SYL/action/citation_signature","submit_replication":"https://pith.science/pith/MPFMSEYFGCQP4TFT44HUF34SYL/action/replication_record"}},"created_at":"2026-07-05T02:38:24.239391+00:00","updated_at":"2026-07-05T02:38:24.239391+00:00"}