{"id":"W4401943669","doi":"10.1109/icdh62654.2024.00034","title":"Using Machine Learning to Track Disability Discourse on Social Media","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Track (disk drive); Computer science; Social media; Artificial intelligence; Natural language processing; World Wide Web","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":[0.0002817644,0.00009955492,0.00009140201,0.0000527892,0.0001833765,0.0002710379,0.0002180762,0.00004191887,0.00008323046],"category_scores_gemma":[0.00006406942,0.00007937207,0.00006692072,0.0003917177,0.00003349427,0.0002419422,0.00008435206,0.0002188928,0.0002537144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008540348,"about_ca_system_score_gemma":0.00002630401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001037811,"about_ca_topic_score_gemma":0.0001554069,"domain_scores_codex":[0.999019,0.00006384529,0.0001182046,0.0003525778,0.0002337075,0.000212617],"domain_scores_gemma":[0.9996282,0.00009882186,0.00001184088,0.0001571478,0.00001369822,0.00009021673],"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":[0.00002360004,0.0001329308,0.0004551668,0.00003699098,0.00002525795,0.00005020481,0.007348021,0.001388305,0.009830173,0.04530096,0.0005550163,0.9348534],"study_design_scores_gemma":[0.0003377942,0.000305143,0.00600144,0.0001120623,0.00002850496,0.00004428252,0.0007137212,0.9336387,0.03031478,0.005256474,0.02241515,0.0008319577],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7973934,0.0000355438,0.1947976,0.002739656,0.001233186,0.0001161031,0.000002686146,0.0008318846,0.002849907],"genre_scores_gemma":[0.9908982,0.000001352097,0.008318239,0.0001018838,0.0002998479,0.000003396243,0.000001568008,0.000009184211,0.0003663534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9340214,"threshold_uncertainty_score":0.3261069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04769561008696838,"score_gpt":0.3219592358438867,"score_spread":0.2742636257569183,"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."}}