{"id":"W7124153997","doi":"10.1109/codit66093.2025.11321615","title":"A BERT Deep Learning Model for Arabic Spam Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton; Université Laval","funders":"","keywords":"Lexical analysis; Deep learning; Arabic; Encoder; Phishing; Inference; Autoencoder; n-gram","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004460438,0.0006992865,0.0004070183,0.0006469098,0.0003030124,0.0006014202,0.0008014835,0.0007325598,0.002397547],"category_scores_gemma":[0.001332693,0.0002812583,0.0003824882,0.00043882,0.0004092745,0.001154586,0.0006060263,0.001275096,0.001145568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008423124,"about_ca_system_score_gemma":0.000860491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007004322,"about_ca_topic_score_gemma":0.00902456,"domain_scores_codex":[0.999861,0.00003234071,0.000007575985,0.00003631742,0.00003491326,0.00002773147],"domain_scores_gemma":[0.9996792,0.0001130046,0.00003493556,0.00003050274,0.0001200602,0.00002240238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005293902,0.0002797885,0.00422959,0.0001139456,0.00008594002,0.000229432,0.00016465,0.5557439,0.01593599,0.01720699,0.01067708,0.3948033],"study_design_scores_gemma":[0.000002906526,0.00001277203,0.00008922767,0.000002918314,0.000005062246,0.0000126362,0.000004012862,0.9966626,0.001095796,0.001733356,0.0003756314,0.000003122509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1115825,0.0008489253,0.8747209,0.001097793,0.0001576709,0.00008590317,0.0005480254,0.00464173,0.006316474],"genre_scores_gemma":[0.8839818,0.0004662974,0.09948202,0.0004625782,0.00006518674,0.0001118223,0.001102722,0.000135318,0.01419221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007004322,"threshold_uncertainty_score":0.0139271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01608217729700309,"score_gpt":0.2519152551136856,"score_spread":0.2358330778166825,"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."}}