{"id":"W4214492748","doi":"10.1155/2022/8467349","title":"An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding","year":2022,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":29,"is_retracted":true,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Taif University","keywords":"Computer science; Word (group theory); Artificial intelligence; Word embedding; Binary classification; Natural language processing; Embedding; Encoder; Context (archaeology); Social media; Machine learning; Speech recognition; Support vector machine; World Wide Web; Linguistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":{"nature":"Retraction","reason":"Concerns/Issues about Peer Review;Investigation by Journal/Publisher;Objections by Author(s);","date":"2/9/2023 0:00","openalex_flagged":true},"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004265074,0.00111022,0.0004023543,0.001457894,0.000241441,0.0005391426,0.0004713522,0.0007788675,0.001356634],"category_scores_gemma":[0.001473368,0.0001817944,0.0005585273,0.000792709,0.0002190647,0.001573651,0.000590315,0.0007579644,0.001323405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004827756,"about_ca_system_score_gemma":0.0004339497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001638135,"about_ca_topic_score_gemma":0.002514233,"domain_scores_codex":[0.9996872,0.00007411621,0.0000258482,0.00009192964,0.00008230325,0.00003862408],"domain_scores_gemma":[0.9992937,0.000247513,0.0001017075,0.00005506468,0.0002725324,0.00002941674],"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":[0.0005672775,0.0004252053,0.009782809,0.000424756,0.0001163516,0.0006335147,0.0002969272,0.02854734,0.0917899,0.001011232,0.006730793,0.8596739],"study_design_scores_gemma":[0.00002339416,0.0005393825,0.009422257,0.00006383619,0.00007125395,0.0003132364,0.0004290707,0.9060295,0.07442268,0.004154174,0.004487124,0.0000441614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5185571,0.001558919,0.4620189,0.001003727,0.0005268281,0.0004292428,0.002991426,0.007422236,0.005491625],"genre_scores_gemma":[0.8327463,0.0007122165,0.1532314,0.0001562796,0.000126378,0.0002549211,0.003453559,0.0001117281,0.009207226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001638135,"threshold_uncertainty_score":0.004538357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1078338663258403,"score_gpt":0.3552851992920706,"score_spread":0.2474513329662303,"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."}}