{"id":"W4412002355","doi":"10.2196/48230","title":"Messaging and Information in Mental Health Communication on Social Media: Computational and Quantitative Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Mental Health via Writing","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mental health; Thematic analysis; Psychology; Psychological intervention; Social media; Cognition; Sentiment analysis; Affect (linguistics); Public health; Content analysis; Discourse analysis; Health communication; Social psychology; Applied psychology; Public relations; Sociology; Political science; Computer science; Qualitative research; Medicine; Linguistics; Psychiatry; Communication; World Wide Web; Nursing; Social science","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":[0.005469303,0.0003972911,0.0003584392,0.004055594,0.0007405279,0.002047949,0.0004947822,0.0004840027,0.001845592],"category_scores_gemma":[0.0323248,0.0002318964,0.0008734735,0.004364253,0.0009583142,0.001638915,0.001160328,0.0006620929,0.0002987675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001637696,"about_ca_system_score_gemma":0.001171329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007044694,"about_ca_topic_score_gemma":0.005489202,"domain_scores_codex":[0.995858,0.002769079,0.0002417465,0.0003519831,0.0006399329,0.000139336],"domain_scores_gemma":[0.9505233,0.04449102,0.001895367,0.001000002,0.001892402,0.0001978225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001121797,0.001632693,0.566256,0.003681347,0.0009053065,0.0005896612,0.03684224,0.04296115,0.008533933,0.02784684,0.007942157,0.3016869],"study_design_scores_gemma":[0.00006683252,0.0005719103,0.374573,0.0005351687,0.0003865279,0.0002741422,0.02304618,0.5642271,0.005916678,0.01934254,0.01090055,0.0001593519],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9466259,0.0002554442,0.04108307,0.0008861762,0.00003677048,0.0008258424,0.005588654,0.0002884986,0.004409572],"genre_scores_gemma":[0.9534026,0.0001094487,0.04221994,0.00005167094,0.00002099899,0.001290411,0.002305196,0.00002433794,0.0005752933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007044694,"threshold_uncertainty_score":0.02892482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06374021438204641,"score_gpt":0.469786205254034,"score_spread":0.4060459908719876,"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."}}