{"id":"W2954935485","doi":"10.1002/spy2.69","title":"Discerning cyber threatening incidents from ordinary events using sentiment analysis and logistic regression","year":2019,"lang":"en","type":"article","venue":"Security and Privacy","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Sentence; Set (abstract data type); Logistic regression; Event (particle physics); Precision and recall; Sentiment analysis; Natural language processing; Artificial intelligence; Recall; Data mining; Machine learning; Psychology","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":[0.002624049,0.001151758,0.0005576576,0.002743794,0.0004570549,0.001477895,0.0005871749,0.0005808693,0.001301456],"category_scores_gemma":[0.008467626,0.0002843783,0.001074703,0.001182305,0.0002606738,0.001367616,0.000659257,0.001174923,0.001113423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006491705,"about_ca_system_score_gemma":0.0005471801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004068192,"about_ca_topic_score_gemma":0.004542402,"domain_scores_codex":[0.9983278,0.0005656283,0.0002650458,0.0003071923,0.0003717797,0.0001626003],"domain_scores_gemma":[0.9944563,0.003224345,0.0007974068,0.0002535498,0.001141666,0.0001267577],"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":[0.001076473,0.001412268,0.3432322,0.0006194363,0.0006350518,0.001026219,0.001259031,0.0785199,0.03369443,0.00210777,0.01903911,0.5173782],"study_design_scores_gemma":[0.00002930693,0.0001985026,0.06099537,0.00005615027,0.00007909182,0.0001637874,0.0006201583,0.9250689,0.008631561,0.001725,0.002395898,0.00003630212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8387799,0.0003488545,0.1489192,0.0008709474,0.0001436756,0.0004016892,0.003247227,0.001725178,0.005563224],"genre_scores_gemma":[0.9281519,0.0001747826,0.06510677,0.0000902852,0.00008397476,0.0001275086,0.005054459,0.00006162303,0.001148655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004068192,"threshold_uncertainty_score":0.01387745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03226740322336508,"score_gpt":0.3019247693715464,"score_spread":0.2696573661481813,"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."}}