{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WUMR3DZTLT4QC2PNCZAQK6YF4N","short_pith_number":"pith:WUMR3DZT","schema_version":"1.0","canonical_sha256":"b5191d8f335cf90169ed1641057b05e36eb0762869ddcd941de9e0ca55037f81","source":{"kind":"arxiv","id":"2504.06917","version":1},"attestation_state":"computed","paper":{"title":"Data Augmentation for Fake Reviews Detection in Multiple Languages and Multiple Domains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Massimo Poesio, Ming Liu","submitted_at":"2025-04-09T14:23:54Z","abstract_excerpt":"With the growth of the Internet, buying habits have changed, and customers have become more dependent on the online opinions of other customers to guide their purchases. Identifying fake reviews thus became an important area for Natural Language Processing (NLP) research. However, developing high-performance NLP models depends on the availability of large amounts of training data, which are often not available for low-resource languages or domains. In this research, we used large language models to generate datasets to train fake review detectors. Our approach was used to generate fake reviews"},"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":"2504.06917","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-04-09T14:23:54Z","cross_cats_sorted":[],"title_canon_sha256":"b67616ac978fa95cada88a6aef8fd98ddb939ba511e2decdcd1e38a64a3e3060","abstract_canon_sha256":"a2502932d42d9756f51d434742ae0a16e8f000ccb3c5ddaabfe89cb7b4f6ea02"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:46:48.572577Z","signature_b64":"QgtwMIIgBJei/fAScoEJvczLbciYTOQ606kjUwm3RcIqMw8WMGhB6vHJONyLo/up93AQMz2UMdVD8RiFx4y8DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b5191d8f335cf90169ed1641057b05e36eb0762869ddcd941de9e0ca55037f81","last_reissued_at":"2026-07-05T10:46:48.571666Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:46:48.571666Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data Augmentation for Fake Reviews Detection in Multiple Languages and Multiple Domains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Massimo Poesio, Ming Liu","submitted_at":"2025-04-09T14:23:54Z","abstract_excerpt":"With the growth of the Internet, buying habits have changed, and customers have become more dependent on the online opinions of other customers to guide their purchases. Identifying fake reviews thus became an important area for Natural Language Processing (NLP) research. However, developing high-performance NLP models depends on the availability of large amounts of training data, which are often not available for low-resource languages or domains. In this research, we used large language models to generate datasets to train fake review detectors. Our approach was used to generate fake reviews"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.06917","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/2504.06917/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":"2504.06917","created_at":"2026-07-05T10:46:48.571732+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.06917v1","created_at":"2026-07-05T10:46:48.571732+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.06917","created_at":"2026-07-05T10:46:48.571732+00:00"},{"alias_kind":"pith_short_12","alias_value":"WUMR3DZTLT4Q","created_at":"2026-07-05T10:46:48.571732+00:00"},{"alias_kind":"pith_short_16","alias_value":"WUMR3DZTLT4QC2PN","created_at":"2026-07-05T10:46:48.571732+00:00"},{"alias_kind":"pith_short_8","alias_value":"WUMR3DZT","created_at":"2026-07-05T10:46:48.571732+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.17654","citing_title":"EVADE-Bench: Multimodal Benchmark for Evaluating and Enhancing Evasive Content Detection","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WUMR3DZTLT4QC2PNCZAQK6YF4N","json":"https://pith.science/pith/WUMR3DZTLT4QC2PNCZAQK6YF4N.json","graph_json":"https://pith.science/api/pith-number/WUMR3DZTLT4QC2PNCZAQK6YF4N/graph.json","events_json":"https://pith.science/api/pith-number/WUMR3DZTLT4QC2PNCZAQK6YF4N/events.json","paper":"https://pith.science/paper/WUMR3DZT"},"agent_actions":{"view_html":"https://pith.science/pith/WUMR3DZTLT4QC2PNCZAQK6YF4N","download_json":"https://pith.science/pith/WUMR3DZTLT4QC2PNCZAQK6YF4N.json","view_paper":"https://pith.science/paper/WUMR3DZT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.06917&json=true","fetch_graph":"https://pith.science/api/pith-number/WUMR3DZTLT4QC2PNCZAQK6YF4N/graph.json","fetch_events":"https://pith.science/api/pith-number/WUMR3DZTLT4QC2PNCZAQK6YF4N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WUMR3DZTLT4QC2PNCZAQK6YF4N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WUMR3DZTLT4QC2PNCZAQK6YF4N/action/storage_attestation","attest_author":"https://pith.science/pith/WUMR3DZTLT4QC2PNCZAQK6YF4N/action/author_attestation","sign_citation":"https://pith.science/pith/WUMR3DZTLT4QC2PNCZAQK6YF4N/action/citation_signature","submit_replication":"https://pith.science/pith/WUMR3DZTLT4QC2PNCZAQK6YF4N/action/replication_record"}},"created_at":"2026-07-05T10:46:48.571732+00:00","updated_at":"2026-07-05T10:46:48.571732+00:00"}