{"id":"W4292839026","doi":"10.2196/37775","title":"Actions Speak Louder Than Words: Sentiment and Topic Analysis of COVID-19 Vaccination on Twitter and Vaccine Uptake","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vaccination; Government (linguistics); Sentiment analysis; Coronavirus disease 2019 (COVID-19); Medicine; Public health; Political science; Family medicine; Virology; Computer science; Artificial intelligence; Internal medicine; Linguistics; Disease; Pathology","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":[0.000948362,0.0001996996,0.0002574788,0.001263713,0.0003747143,0.001085646,0.0001355943,0.0002566998,0.001528679],"category_scores_gemma":[0.005199434,0.000107318,0.0003542615,0.001352709,0.0002399834,0.0009331506,0.0005501879,0.0004337658,0.0004824256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004102867,"about_ca_system_score_gemma":0.0002814565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004990145,"about_ca_topic_score_gemma":0.006749486,"domain_scores_codex":[0.9994919,0.0001827146,0.00006109467,0.00008125856,0.0001075475,0.00007539718],"domain_scores_gemma":[0.9951166,0.002684516,0.001304898,0.0000957887,0.0005514952,0.0002466622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005009533,0.0001463001,0.9306622,0.0003994287,0.0001335677,0.0002794137,0.009536657,0.0007116268,0.006240297,0.0006336581,0.003401162,0.04735468],"study_design_scores_gemma":[0.000007349011,0.0001122182,0.9801096,0.00006561219,0.00006600034,0.0001351522,0.005935108,0.007980504,0.001123232,0.0004377503,0.004004362,0.00002309887],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939467,0.000236075,0.0005722785,0.0004519488,0.00003627557,0.00003799987,0.001842427,0.00001912894,0.00285725],"genre_scores_gemma":[0.9966009,0.0001879667,0.0008187555,0.00007628184,0.00006652786,0.00005289465,0.001333092,0.000008906941,0.0008546262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004990145,"threshold_uncertainty_score":0.009922147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1000205607286296,"score_gpt":0.4537657019941426,"score_spread":0.353745141265513,"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."}}