{"id":"W4386810309","doi":"10.18280/ria.370413","title":"Enhancing Cyberbullying Detection on Indonesian Twitter: Leveraging FastText for Feature Expansion and Hybrid Approach Applying CNN and BiLSTM","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Indonesian; Feature (linguistics); Computer science; Artificial intelligence; Pattern recognition (psychology)","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":[0.0003398021,0.001258032,0.0004815054,0.001128964,0.0003330609,0.0005228818,0.0006665692,0.0005102402,0.001609776],"category_scores_gemma":[0.001343162,0.0001791976,0.0005278956,0.0006020026,0.0002185999,0.00119153,0.0009545799,0.0007678183,0.001111376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004483949,"about_ca_system_score_gemma":0.000500688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007727497,"about_ca_topic_score_gemma":0.01250276,"domain_scores_codex":[0.9997672,0.00003230297,0.00001540195,0.00006558583,0.00005031039,0.00006918998],"domain_scores_gemma":[0.9997194,0.00007308238,0.00003854728,0.00003646796,0.0001070411,0.00002547054],"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":[0.000965851,0.0009931126,0.03781657,0.0003557182,0.0002063446,0.0008844255,0.0003101267,0.07207029,0.05099144,0.0008995354,0.01929212,0.8152145],"study_design_scores_gemma":[0.00002647963,0.0002642026,0.01190782,0.00003768348,0.00006633042,0.0001773099,0.0002385249,0.9580693,0.02390954,0.001179981,0.004091757,0.00003109535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8444737,0.001941024,0.1284316,0.001265918,0.000562792,0.0002402059,0.004335641,0.008962863,0.00978626],"genre_scores_gemma":[0.9550351,0.000379833,0.03200359,0.0002702717,0.0001008942,0.000120283,0.006101795,0.0000992993,0.005888967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007727497,"threshold_uncertainty_score":0.01536506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03319803522907172,"score_gpt":0.2519311943709145,"score_spread":0.2187331591418428,"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."}}