{"id":"W4285345752","doi":"10.2196/34315","title":"Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department of Biotechnology, Ministry of Science and Technology, India","keywords":"Globe; Variety (cybernetics); Government (linguistics); Social media; Observational study; Psychology; Medicine; Computer science; Linguistics; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001054344,0.0001421822,0.0003644596,0.000198424,0.0006354245,0.00001195714,0.0003462115,0.0000778657,0.001919029],"category_scores_gemma":[0.0002965969,0.0001240892,0.00007375403,0.0004043894,0.00003787064,0.000105087,0.0001757907,0.000300835,0.000006360629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002102018,"about_ca_system_score_gemma":0.000335527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005674716,"about_ca_topic_score_gemma":0.0008408903,"domain_scores_codex":[0.9980326,0.0004850794,0.0004109078,0.0003406668,0.0003921826,0.0003386079],"domain_scores_gemma":[0.9987085,0.0006220671,0.0002454363,0.0002142075,0.00006625005,0.0001434656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000371864,0.0003523187,0.9411024,0.00000620472,0.00004171551,0.00003160472,0.0223015,0.002159687,0.000009043136,0.01441567,0.01902772,0.0001802632],"study_design_scores_gemma":[0.001369589,0.00201303,0.9589345,0.000005304393,0.00002190087,0.000008329306,0.007700899,0.00006628399,0.00000138726,0.0008029796,0.02890281,0.0001729653],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829646,0.00002675535,0.0001037418,0.0129313,0.0001170418,0.0002717726,0.000009461291,0.00005680856,0.003518531],"genre_scores_gemma":[0.9925215,0.000002802527,0.0002210875,0.005604378,0.0002180704,0.0002324267,0.00002662986,0.00001012689,0.001162959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01783212,"threshold_uncertainty_score":0.9989933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1664919910399237,"score_gpt":0.4254794763902147,"score_spread":0.258987485350291,"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."}}