{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005108881,0.00007989011,0.0001401321,0.0001884464,0.0003278689,0.0001215156,0.0001108389,0.0000666398,0.0001413545],"category_scores_gemma":[0.0002163041,0.00007217239,0.0000457841,0.001210657,0.0001286336,0.0001869118,0.00001544868,0.000210377,0.000008135624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005929707,"about_ca_system_score_gemma":0.0005081197,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03639084,"about_ca_topic_score_gemma":0.01548408,"domain_scores_codex":[0.9988129,0.0003258789,0.0001637332,0.0002057046,0.0002940094,0.0001977883],"domain_scores_gemma":[0.9987851,0.0008219891,0.0000884276,0.0001033531,0.000118458,0.0000826446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0000109739,0.00001952432,0.02575377,0.0001415447,0.00006255524,6.604426e-7,0.863615,0.000003999833,0.00001516323,0.0005747812,0.001387821,0.1084142],"study_design_scores_gemma":[0.0004290866,0.00005827266,0.4416206,0.0005344136,0.0009317876,2.054639e-7,0.4982165,0.00168458,0.00005819272,0.0004675681,0.05536968,0.0006291155],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9149378,0.06948645,0.000006705909,0.0130736,0.001304618,0.0006246724,0.00001437172,0.00009367903,0.0004581063],"genre_scores_gemma":[0.9921767,0.003480568,0.00006843557,0.0005578061,0.001058574,0.002234622,0.0000105142,0.00001038074,0.0004024525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4158668,"threshold_uncertainty_score":0.9700259,"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."}}