{"id":"W2996568036","doi":"10.1109/iemcon.2019.8936148","title":"Spam Review Detection Using Deep Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Machine learning; Perceptron; Deep learning; Support vector machine; Convolutional neural network; Perplexity; Recurrent neural network; Naive Bayes classifier; Focus (optics); Artificial neural network; Language model","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007725216,0.000597966,0.0005843833,0.001767618,0.0003017595,0.0007536878,0.0006402783,0.0007990356,0.000833212],"category_scores_gemma":[0.002816982,0.0002646322,0.0003940965,0.0006027714,0.0002146535,0.0008634519,0.0005240439,0.0006066748,0.0008371933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008005736,"about_ca_system_score_gemma":0.00072633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004522417,"about_ca_topic_score_gemma":0.005744947,"domain_scores_codex":[0.9993364,0.000137385,0.00004823689,0.0001226572,0.0002724281,0.00008279891],"domain_scores_gemma":[0.9980626,0.0003362075,0.0003797606,0.0001192792,0.001018157,0.0000840238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005609038,0.0005037991,0.02213306,0.0004204057,0.0002113169,0.0006527783,0.0002277648,0.05665868,0.03830904,0.002403181,0.02136336,0.8565558],"study_design_scores_gemma":[0.0000123365,0.0001215198,0.004424817,0.00002248617,0.00004090471,0.0001684871,0.00003703338,0.971002,0.01911884,0.001357792,0.003674856,0.00001886503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3506318,0.006113077,0.6207013,0.001441849,0.0004472608,0.0003553592,0.0009284072,0.008256595,0.01112438],"genre_scores_gemma":[0.9130851,0.0009388186,0.07481919,0.0004350268,0.0002105804,0.00008267314,0.001043159,0.00005916153,0.009326207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004522417,"threshold_uncertainty_score":0.008992195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01473273037901539,"score_gpt":0.2406082519381134,"score_spread":0.225875521559098,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}