{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002713827,0.00006051232,0.00008566798,0.0000455612,0.00007943776,0.00007312779,0.0001873787,0.00003163409,0.0001123837],"category_scores_gemma":[0.00003450933,0.00005410034,0.00004227908,0.000306335,0.000003870276,0.0004229659,0.00006229468,0.0001239818,0.000428748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003495567,"about_ca_system_score_gemma":0.000008743396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007288987,"about_ca_topic_score_gemma":0.000009683867,"domain_scores_codex":[0.9993975,0.0000536941,0.0001013633,0.0001970899,0.0001313624,0.0001189862],"domain_scores_gemma":[0.9996315,0.00002607748,0.00005074034,0.0002243228,0.00003621423,0.00003118976],"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":[0.000004714632,0.00002583084,0.005199705,0.0004866469,0.00002041182,0.000004161066,0.0002551453,0.003035323,0.04562715,0.003456335,0.0000949961,0.9417896],"study_design_scores_gemma":[0.0001557985,0.0001302166,0.001732394,0.0003299761,0.00001103621,0.00007730384,0.00001158683,0.9537795,0.01249937,0.000481285,0.03054954,0.0002420123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0684861,0.0017986,0.9213421,0.0001291444,0.0008899957,0.0001456645,1.308437e-8,0.0003154049,0.006892934],"genre_scores_gemma":[0.9890862,0.0003291402,0.009318814,0.0005114811,0.00006378733,0.000002082415,2.165754e-7,0.00000589038,0.0006823961],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9507442,"threshold_uncertainty_score":0.5510831,"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."}}