{"id":"W4293070677","doi":"10.2196/38485","title":"Negative COVID-19 Vaccine Information on Twitter: Content Analysis","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo; McMaster University","funders":"","keywords":"Misinformation; Pandemic; Social media; Vaccination; Government (linguistics); Coronavirus disease 2019 (COVID-19); Medicine; Public health; Political science; Virology; World Wide Web; Computer science; Disease; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00177076,0.0003246215,0.0003996249,0.007736069,0.0007257662,0.001434143,0.0004144238,0.0004278792,0.001963253],"category_scores_gemma":[0.01107748,0.0001939628,0.0004992394,0.007388022,0.0004420908,0.001778082,0.001290476,0.0004586667,0.001173317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001112206,"about_ca_system_score_gemma":0.0006668501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008757761,"about_ca_topic_score_gemma":0.01089742,"domain_scores_codex":[0.998265,0.0004433114,0.0003032482,0.0002940596,0.0004976044,0.0001968676],"domain_scores_gemma":[0.9880579,0.006650799,0.002615609,0.0004324958,0.001981818,0.0002612953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004591831,0.000138294,0.8421623,0.00245262,0.0001774173,0.0007467881,0.02050242,0.001223444,0.007704261,0.001382597,0.02210923,0.1009414],"study_design_scores_gemma":[0.00001724789,0.0001115934,0.931156,0.0004418833,0.0001485572,0.0006066371,0.01675372,0.01739162,0.003578862,0.001215387,0.02847938,0.00009913133],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.952222,0.0005424109,0.00259955,0.0007554093,0.00008352147,0.0005312407,0.03807455,0.0001482675,0.005042911],"genre_scores_gemma":[0.9532119,0.0007561311,0.01005462,0.0002227272,0.000167949,0.001087689,0.03167713,0.0000785882,0.002743243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008757761,"threshold_uncertainty_score":0.01741362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09026226955581243,"score_gpt":0.3780310972798045,"score_spread":0.287768827723992,"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."}}