{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001631452,0.0009473257,0.0009027421,0.001446018,0.0006538682,0.0007401102,0.001123664,0.001700289,0.0006541151],"category_scores_gemma":[0.005597771,0.0003363403,0.0005715267,0.0008238648,0.0006639184,0.001520124,0.000812791,0.001098232,0.0005864964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008273023,"about_ca_system_score_gemma":0.0004821916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004647997,"about_ca_topic_score_gemma":0.005439608,"domain_scores_codex":[0.9990414,0.0003177339,0.00005411177,0.0002866744,0.0001955669,0.0001045115],"domain_scores_gemma":[0.9971629,0.001078106,0.0003607111,0.0003588955,0.0009525621,0.00008697557],"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":[0.001069226,0.0004974309,0.01866049,0.0001620398,0.0001276082,0.0004074998,0.0004203447,0.1897623,0.02913977,0.004010557,0.007246138,0.7484967],"study_design_scores_gemma":[0.000006319581,0.00003298947,0.001085068,0.00000494795,0.00001590989,0.0000343817,0.0000135275,0.9944645,0.003078034,0.0008984951,0.000358621,0.00000732023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2825859,0.000901508,0.7087253,0.0005903287,0.0001640641,0.000185514,0.0002648587,0.002390478,0.004191906],"genre_scores_gemma":[0.8827155,0.000254539,0.1116357,0.0003028531,0.0002551689,0.0001440291,0.0008044817,0.00005908742,0.003828672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004647997,"threshold_uncertainty_score":0.009241879,"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."}}