{"id":"W4399349276","doi":"10.2196/58259","title":"Natural Language Processing for Depression Prediction on Sina Weibo: Method Study and Analysis","year":2024,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Mental Health via Writing","field":"Psychology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Encoder; Artificial intelligence; Social media; Depression (economics); Natural language processing; Psychology; Semantics (computer science); World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003097035,0.000795002,0.0003621116,0.002228308,0.0008110602,0.00073231,0.0006839001,0.0005676651,0.003246397],"category_scores_gemma":[0.008431184,0.0002221161,0.0009992283,0.001781924,0.0005165518,0.001047932,0.0008509277,0.0009177716,0.001015098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001480909,"about_ca_system_score_gemma":0.00133199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02810897,"about_ca_topic_score_gemma":0.02879574,"domain_scores_codex":[0.9985443,0.00067205,0.0001238924,0.0003026393,0.0002589367,0.00009822497],"domain_scores_gemma":[0.9933228,0.005406883,0.0002279292,0.0003885983,0.0005643771,0.0000894093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001599318,0.001387294,0.3195636,0.0008448013,0.0004602073,0.002680582,0.002463955,0.1294672,0.00663179,0.01359892,0.02727858,0.4940237],"study_design_scores_gemma":[0.00003779058,0.00008685796,0.03752112,0.00004143261,0.00004680116,0.000278148,0.000708469,0.9509726,0.002261612,0.003941887,0.004057533,0.00004585096],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.788635,0.001319912,0.1879377,0.001182073,0.0001782734,0.001302603,0.0119127,0.001966772,0.005565122],"genre_scores_gemma":[0.8839141,0.0003557228,0.09929694,0.0001133188,0.00005854942,0.001245182,0.0122341,0.00008656592,0.002695484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02810897,"threshold_uncertainty_score":0.05589068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03052989767743555,"score_gpt":0.4966366071649048,"score_spread":0.4661067094874692,"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."}}