{"id":"W3115116643","doi":"10.1109/ictai50040.2020.00087","title":"Deep Learning Ensembles for Hate Speech Detection","year":2020,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Offensive; Artificial intelligence; Class (philosophy); Task (project management); Ensemble learning; Natural language processing; Word (group theory); Speech recognition; Arabic; Embedding; Word embedding; Ensemble forecasting; Machine learning; Pattern recognition (psychology); Linguistics; Mathematics","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":[0.0001303613,0.00008919316,0.00008959839,0.00004122211,0.0001961837,0.0001549088,0.0002313498,0.00005175083,0.00001987963],"category_scores_gemma":[0.0001015303,0.0000832909,0.00006591184,0.0002576817,0.000008412982,0.0002891241,0.0000602478,0.0001105305,0.0001667284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001666168,"about_ca_system_score_gemma":0.000009730054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000208017,"about_ca_topic_score_gemma":0.00002640245,"domain_scores_codex":[0.9992149,0.00003050096,0.0001221896,0.0002968162,0.0001249227,0.0002106015],"domain_scores_gemma":[0.9996204,0.00004655891,0.00004375018,0.0001238374,0.00006046109,0.0001049553],"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":[0.00001797729,0.00000774854,0.00002987635,0.00001648513,0.00001017685,0.00000504,0.0003561699,0.0007361844,0.06318295,0.001791038,0.0001196508,0.9337267],"study_design_scores_gemma":[0.0002486864,0.0003195163,0.00007485267,0.000003204867,0.000004155008,0.00001585813,0.0000615817,0.5513212,0.4174848,0.0008051567,0.02951986,0.0001410928],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01740443,0.00003055921,0.9781482,0.001075576,0.0002249325,0.0001635398,1.003308e-7,0.0006203086,0.002332375],"genre_scores_gemma":[0.9239439,0.00001115412,0.07464246,0.0005830058,0.0001706944,0.00001853014,0.000001022381,0.00001011693,0.0006191676],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9335856,"threshold_uncertainty_score":0.3396504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01723717314677962,"score_gpt":0.2247345402335494,"score_spread":0.2074973670867698,"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."}}