{"id":"W2947983942","doi":"","title":"Opinion Spam Detection with Attention-Based Neural Networks","year":2019,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Scalability; Machine learning; Point (geometry); Feature (linguistics); Damages; Data science; Data mining; Database","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":[0.0008295366,0.0008135026,0.0007160293,0.001200738,0.0003641817,0.0007158045,0.0008459368,0.001066377,0.001024035],"category_scores_gemma":[0.002688478,0.0003214035,0.0006210234,0.0005358482,0.0003774292,0.0008562498,0.0005912447,0.0009683648,0.0005008684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001007337,"about_ca_system_score_gemma":0.0004205403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007372823,"about_ca_topic_score_gemma":0.005166425,"domain_scores_codex":[0.9995377,0.0001114158,0.00002693073,0.0001140165,0.000110234,0.00009978174],"domain_scores_gemma":[0.9989353,0.000370486,0.000182859,0.0000638368,0.0004019819,0.00004551693],"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":[0.00100697,0.0006957282,0.01285532,0.0001655065,0.0002733229,0.0004585255,0.0002750917,0.2686456,0.02020993,0.004282529,0.009181463,0.68195],"study_design_scores_gemma":[0.000004318417,0.00002781868,0.0005766212,0.000003976791,0.00001488779,0.0000173223,0.000005985622,0.9972576,0.001204172,0.0007164156,0.0001669493,0.000003882091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3886749,0.002699248,0.5910131,0.001758612,0.0004671823,0.0002031178,0.0003294391,0.003593597,0.01126081],"genre_scores_gemma":[0.9716284,0.0002638762,0.02370441,0.0003338872,0.000255742,0.00004493884,0.0002087506,0.0000267752,0.003533168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007372823,"threshold_uncertainty_score":0.01465982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00972640546641135,"score_gpt":0.2124582192948141,"score_spread":0.2027318138284027,"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."}}