{"id":"W6991711041","doi":"","title":"Identifying and Analyzing Provocative Text: An XAI Approach to Classification and Feature Selection","year":2025,"lang":"en","type":"article","venue":"DiVA at Umeå University (Umeå University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Ambiguity; Bayesian probability; Feature selection; Feature (linguistics); Task (project management); Model selection; Naive Bayes classifier; Discriminator; Bayesian inference","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":[0.0001797703,0.0001705924,0.0002038236,0.001172088,0.0009290895,0.0002014541,0.0005037151,0.0001109972,0.000004638608],"category_scores_gemma":[0.00001258692,0.0002091299,0.00006180463,0.002204855,0.00007533796,0.001289903,0.0006615481,0.0001619151,0.000005081175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000329587,"about_ca_system_score_gemma":0.00006539899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001014719,"about_ca_topic_score_gemma":0.00009046803,"domain_scores_codex":[0.9986073,0.00016363,0.00008723403,0.0007389073,0.0001695734,0.0002333704],"domain_scores_gemma":[0.9992524,0.00004709509,0.00010555,0.000286725,0.0001392007,0.0001690282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000292629,0.0004855016,0.3535187,0.0001753489,0.0008122798,0.00006505647,0.01681275,0.0004802154,0.01496743,0.5680313,0.003603114,0.04075563],"study_design_scores_gemma":[0.00305977,0.000283161,0.6024746,0.0002175967,0.00069818,0.00002220308,0.03081618,0.2575248,0.001805588,0.0004809078,0.1011483,0.001468656],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4996689,0.00007726342,0.4818399,0.001084952,0.0001148339,0.000344934,0.000004966664,0.0002009163,0.01666334],"genre_scores_gemma":[0.9609236,0.00007724018,0.01944027,0.00007589703,0.0000200296,1.795688e-7,0.00002774458,0.000006096597,0.01942891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5675504,"threshold_uncertainty_score":0.852807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02184279041342975,"score_gpt":0.2354069496545688,"score_spread":0.213564159241139,"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."}}