{"id":"W4409400095","doi":"10.2196/70576","title":"Exploring Topics, Emotions, and Sentiments in Health Organization Posts and Public Responses on Instagram: Content Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Sentiment analysis; Psychology; Public health; Internet privacy; Public relations; Computer science; Political science; World Wide Web; Medicine; Artificial intelligence","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":[0.00260728,0.0004028422,0.0003216045,0.004924008,0.0006729211,0.001484393,0.00028547,0.0003596187,0.001447285],"category_scores_gemma":[0.009295936,0.0001424617,0.0004783891,0.004770292,0.0005617263,0.001340017,0.001290526,0.0004800876,0.0004838903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008911454,"about_ca_system_score_gemma":0.0005287333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002568996,"about_ca_topic_score_gemma":0.003931809,"domain_scores_codex":[0.998038,0.000834744,0.0001733325,0.0002557205,0.0004780669,0.0002201692],"domain_scores_gemma":[0.9881058,0.008482222,0.00156429,0.0003287638,0.001308853,0.0002100572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00122157,0.000606745,0.5845688,0.002767656,0.0003341582,0.0009954418,0.07457307,0.002622829,0.04086639,0.002804767,0.01781254,0.2708261],"study_design_scores_gemma":[0.00002655775,0.0003290096,0.8999756,0.0002367053,0.0001663872,0.0004212968,0.04413008,0.02603431,0.008206571,0.00170515,0.01866113,0.0001072453],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842366,0.0001857087,0.005118164,0.0004053244,0.00003772221,0.0005065408,0.005739656,0.0001531274,0.003617023],"genre_scores_gemma":[0.977055,0.0002135089,0.01393372,0.0001387124,0.0001165874,0.001384683,0.005508522,0.00004772409,0.001601585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004924008,"threshold_uncertainty_score":0.01378882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2395757449381755,"score_gpt":0.4097783927157247,"score_spread":0.1702026477775492,"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."}}