{"id":"W4390824166","doi":"10.1515/9782766301850-015","title":"Agenda-Setting of Domestic Violence on Social Media: Machine Learning and Content Analysis Approach","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University of Toronto","funders":"","keywords":"Social media; Content (measure theory); Content analysis; Domestic violence; Computer science; Sociology; Media studies; Public relations; Psychology; Criminology; Political science; Internet privacy; World Wide Web; Social science; Medicine; Medical emergency; Human factors and ergonomics; Mathematics; Poison control","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":[0.009951032,0.0005869998,0.0007584192,0.005869135,0.002786946,0.01125054,0.002297229,0.003436415,0.006027574],"category_scores_gemma":[0.01932889,0.0005547931,0.0009252297,0.005617321,0.007564239,0.01606131,0.003645228,0.004147514,0.0008504122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006036319,"about_ca_system_score_gemma":0.004560015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003303157,"about_ca_topic_score_gemma":0.004722353,"domain_scores_codex":[0.9911137,0.006565221,0.000321602,0.0005155276,0.001152492,0.0003315324],"domain_scores_gemma":[0.9830831,0.01426227,0.0006890211,0.0005423498,0.001064761,0.0003584344],"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":[0.00002215328,0.0000508876,0.001080271,0.0002872974,0.00002931999,0.0001686397,0.009028304,0.001537654,0.0002162716,0.9267575,0.01219412,0.04862763],"study_design_scores_gemma":[0.0000136263,0.00001632573,0.001552725,0.000905358,0.0000206785,0.0001129799,0.01428887,0.01150766,0.0007307009,0.9166211,0.05419798,0.00003204578],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04153626,0.01078023,0.3484483,0.08307046,0.00144758,0.001139586,0.001619482,0.0003525811,0.5116055],"genre_scores_gemma":[0.7613782,0.006598582,0.1836466,0.003566233,0.001069221,0.00140224,0.00149927,0.0002478689,0.04059171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01125054,"threshold_uncertainty_score":0.05262667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1513093078353774,"score_gpt":0.3754243571679383,"score_spread":0.2241150493325609,"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."}}