{"id":"W2925597902","doi":"10.5539/cis.v12n2p87","title":"Opinion Spam Detection based on Annotation Extension and Neural Networks","year":2019,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Annotation; Reputation; Machine learning; Scheme (mathematics); Artificial intelligence; Sentiment analysis; Classifier (UML); Artificial neural network; Information retrieval; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005570699,0.00009567998,0.00007802898,0.000326208,0.0002924159,0.0006999641,0.0002159655,0.00004381442,0.000001516423],"category_scores_gemma":[0.00002311925,0.00008403137,0.00001625459,0.0006496097,0.00007012479,0.007799832,0.0001144838,0.0001062183,0.00001523009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002895749,"about_ca_system_score_gemma":0.0000224283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008129116,"about_ca_topic_score_gemma":3.213361e-7,"domain_scores_codex":[0.9990587,0.00002670349,0.0001791895,0.0002509539,0.0003194662,0.0001650325],"domain_scores_gemma":[0.9993481,0.00006133024,0.00010094,0.0002585386,0.0001467907,0.00008426989],"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":[0.00002490866,0.00001173221,0.001148598,0.00002233541,0.000001036846,1.974798e-7,0.0005893703,0.1233514,0.0005167568,0.004191368,0.00006384254,0.8700784],"study_design_scores_gemma":[0.0002615681,0.0002685433,0.0816226,0.00001870531,6.086065e-7,0.0000135233,0.000006880312,0.9165056,0.0003397437,0.00007819721,0.0007840669,0.0001000172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1430502,0.00001269542,0.854351,0.0002047347,0.001785437,0.0001557643,2.71703e-7,0.0001028338,0.0003369984],"genre_scores_gemma":[0.9942458,0.00001417772,0.004262313,0.001398708,0.00006914611,0.000003132279,0.000002648209,0.000001867824,0.000002196012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8699784,"threshold_uncertainty_score":0.674977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009069388388157556,"score_gpt":0.2208288503266041,"score_spread":0.2117594619384466,"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."}}