{"id":"W4396692870","doi":"10.2196/52061","title":"Pediatric Cancer Communication on Twitter: Natural Language Processing and Qualitative Content Analysis","year":2024,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute","keywords":"Sentiment analysis; Social media; Lexicon; Content analysis; Computer science; Psychology; Medicine; Artificial intelligence; 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":[0.007237997,0.0003206163,0.000387715,0.00330965,0.001612745,0.001892862,0.0006928939,0.0005117686,0.004016713],"category_scores_gemma":[0.02232434,0.0002645325,0.0003953073,0.004566743,0.001639821,0.001936667,0.002330012,0.0007720425,0.0008265395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003135159,"about_ca_system_score_gemma":0.002560658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006237043,"about_ca_topic_score_gemma":0.009272011,"domain_scores_codex":[0.9947626,0.003338462,0.0003414004,0.0004102261,0.0007102114,0.0004370764],"domain_scores_gemma":[0.9792475,0.015919,0.001534912,0.0005542925,0.002514594,0.0002296821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003065511,0.0002307231,0.07181592,0.002195904,0.0000300034,0.001452402,0.81692,0.0009417924,0.00930458,0.008668176,0.01322401,0.07490997],"study_design_scores_gemma":[0.00004152342,0.0001566262,0.09965482,0.001083498,0.00003854172,0.0003780273,0.8248365,0.009058252,0.00636018,0.007319282,0.05096392,0.0001088433],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9604078,0.0002217696,0.01293079,0.002230842,0.0000456226,0.003002997,0.01221442,0.000101217,0.008844522],"genre_scores_gemma":[0.9361733,0.0004493063,0.03964808,0.0007920192,0.00005008881,0.01307378,0.006301452,0.0001340724,0.003377925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007237997,"threshold_uncertainty_score":0.03827864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2114973865631683,"score_gpt":0.5508799106974672,"score_spread":0.3393825241342989,"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."}}