{"id":"W4410447618","doi":"10.3233/shti250612","title":"Utilizing Large Language Models to Monitor Social Media for Disability: An Analysis of Sentiment and Disability Models in Tweets","year":2025,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Text Readability and Simplification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"York University","keywords":"Sentiment analysis; Social media; Computer science; Social model of disability; Language model; Natural language processing; Artificial intelligence; Psychology; World Wide Web; Psychiatry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001282453,0.0001033866,0.0004826637,0.0004050294,0.0001608196,0.00001269799,0.0002305457,0.0001175887,1.469889e-7],"category_scores_gemma":[0.0001951105,0.00009534456,0.00002976324,0.00137715,0.0003742098,0.000417756,0.0003649036,0.000127575,4.095482e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001739509,"about_ca_system_score_gemma":0.00003093389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006842069,"about_ca_topic_score_gemma":0.001657996,"domain_scores_codex":[0.9986115,0.00005658293,0.0007731867,0.0002025274,0.00009402832,0.0002621428],"domain_scores_gemma":[0.9990796,0.0003398422,0.0001287498,0.0003563872,0.00006177784,0.00003365637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003823385,0.0003974709,0.08075871,0.001700566,0.0001404897,1.526493e-7,0.2046001,0.002369468,0.000003249929,0.5234005,0.00001271701,0.1865783],"study_design_scores_gemma":[0.0004019354,0.00008583023,0.05025503,0.00006437418,0.00002886737,2.029341e-7,0.08742935,0.7437407,0.00004211587,0.1178192,0.0000220417,0.0001103448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9107383,0.0005877996,0.08498725,0.002982851,0.00005981067,0.000535308,0.00003730463,0.00004889831,0.00002244212],"genre_scores_gemma":[0.9866387,0.0001554403,0.01294656,0.0001446228,0.000003524264,0.0001020822,0.000006511764,0.000001603262,9.253145e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7413712,"threshold_uncertainty_score":0.3888038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0679866848074883,"score_gpt":0.3937030103743477,"score_spread":0.3257163255668594,"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."}}