{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:SPFEFDW5C7SMYOXPPVE55GZP6X","short_pith_number":"pith:SPFEFDW5","schema_version":"1.0","canonical_sha256":"93ca428edd17e4cc3aef7d49de9b2ff5fe020a4785dd57c751cf3892e6b75006","source":{"kind":"arxiv","id":"1805.10364","version":1},"attestation_state":"computed","paper":{"title":"Detecting Deceptive Reviews using Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.IR","cs.LG"],"primary_cat":"cs.CR","authors_text":"Aravind Machiry, Christopher Kruegel, Giovanni Vigna, Hojjat Aghakhani, Shirin Nilizadeh","submitted_at":"2018-05-25T21:06:56Z","abstract_excerpt":"In the past few years, consumer review sites have become the main target of deceptive opinion spam, where fictitious opinions or reviews are deliberately written to sound authentic. Most of the existing work to detect the deceptive reviews focus on building supervised classifiers based on syntactic and lexical patterns of an opinion. With the successful use of Neural Networks on various classification applications, in this paper, we propose FakeGAN a system that for the first time augments and adopts Generative Adversarial Networks (GANs) for a text classification task, in particular, detectin"},"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":"1805.10364","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2018-05-25T21:06:56Z","cross_cats_sorted":["cs.AI","cs.CL","cs.IR","cs.LG"],"title_canon_sha256":"9729278ce3106561092d0a742b0a6b8c11c2b68094e5e235700423237c16be03","abstract_canon_sha256":"faeb575f10ffdeca846078c41a909f9b48151b072a79351a7b3fdd6e5870f983"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:52.801583Z","signature_b64":"RJOwxE7lYXhnFfmNhC9Gq3lLCgRNaVyimzALmTfPHTQTdG07am7WvuKad9VqXa7BkGVGXThnbHR6epMi2GfvDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93ca428edd17e4cc3aef7d49de9b2ff5fe020a4785dd57c751cf3892e6b75006","last_reissued_at":"2026-05-18T00:14:52.800811Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:52.800811Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detecting Deceptive Reviews using Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.IR","cs.LG"],"primary_cat":"cs.CR","authors_text":"Aravind Machiry, Christopher Kruegel, Giovanni Vigna, Hojjat Aghakhani, Shirin Nilizadeh","submitted_at":"2018-05-25T21:06:56Z","abstract_excerpt":"In the past few years, consumer review sites have become the main target of deceptive opinion spam, where fictitious opinions or reviews are deliberately written to sound authentic. Most of the existing work to detect the deceptive reviews focus on building supervised classifiers based on syntactic and lexical patterns of an opinion. With the successful use of Neural Networks on various classification applications, in this paper, we propose FakeGAN a system that for the first time augments and adopts Generative Adversarial Networks (GANs) for a text classification task, in particular, detectin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10364","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":""},"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":"1805.10364","created_at":"2026-05-18T00:14:52.800931+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.10364v1","created_at":"2026-05-18T00:14:52.800931+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10364","created_at":"2026-05-18T00:14:52.800931+00:00"},{"alias_kind":"pith_short_12","alias_value":"SPFEFDW5C7SM","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"SPFEFDW5C7SMYOXP","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"SPFEFDW5","created_at":"2026-05-18T12:32:53.628368+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/SPFEFDW5C7SMYOXPPVE55GZP6X","json":"https://pith.science/pith/SPFEFDW5C7SMYOXPPVE55GZP6X.json","graph_json":"https://pith.science/api/pith-number/SPFEFDW5C7SMYOXPPVE55GZP6X/graph.json","events_json":"https://pith.science/api/pith-number/SPFEFDW5C7SMYOXPPVE55GZP6X/events.json","paper":"https://pith.science/paper/SPFEFDW5"},"agent_actions":{"view_html":"https://pith.science/pith/SPFEFDW5C7SMYOXPPVE55GZP6X","download_json":"https://pith.science/pith/SPFEFDW5C7SMYOXPPVE55GZP6X.json","view_paper":"https://pith.science/paper/SPFEFDW5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.10364&json=true","fetch_graph":"https://pith.science/api/pith-number/SPFEFDW5C7SMYOXPPVE55GZP6X/graph.json","fetch_events":"https://pith.science/api/pith-number/SPFEFDW5C7SMYOXPPVE55GZP6X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SPFEFDW5C7SMYOXPPVE55GZP6X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SPFEFDW5C7SMYOXPPVE55GZP6X/action/storage_attestation","attest_author":"https://pith.science/pith/SPFEFDW5C7SMYOXPPVE55GZP6X/action/author_attestation","sign_citation":"https://pith.science/pith/SPFEFDW5C7SMYOXPPVE55GZP6X/action/citation_signature","submit_replication":"https://pith.science/pith/SPFEFDW5C7SMYOXPPVE55GZP6X/action/replication_record"}},"created_at":"2026-05-18T00:14:52.800931+00:00","updated_at":"2026-05-18T00:14:52.800931+00:00"}