{"id":"W3015671793","doi":"10.2196/18700","title":"Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":208,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Microblogging; Outbreak; Coronavirus disease 2019 (COVID-19); Pandemic; China; Observational study; Public health; Data collection; Geography; Demography; Medicine; Environmental health; Statistics; Disease; Computer science; Infectious disease (medical specialty); Virology; Sociology; Mathematics; 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":[0.002603717,0.000120514,0.0003784514,0.00007227076,0.001276537,0.000196096,0.0004692352,0.00005361814,0.0000334506],"category_scores_gemma":[0.006330185,0.00007231841,0.0000462582,0.001763085,0.0002795825,0.0005518337,0.0002397717,0.0001486538,5.018165e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001151601,"about_ca_system_score_gemma":0.0009609001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009135157,"about_ca_topic_score_gemma":0.01527916,"domain_scores_codex":[0.9979214,0.0003324936,0.0004716839,0.0002417082,0.0006973126,0.000335366],"domain_scores_gemma":[0.9980779,0.0005255573,0.0004334049,0.0002625447,0.0001606146,0.0005400323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000247026,0.00001631629,0.7641411,0.00002214795,0.00006945869,1.726713e-7,0.2333636,0.000001187036,4.708521e-7,0.0004174462,0.0005379588,0.001405464],"study_design_scores_gemma":[0.0005151125,0.00003592423,0.9388394,0.000001347058,0.000002686915,3.174234e-7,0.05340991,0.0006043511,1.293215e-8,0.00001036313,0.006506335,0.00007420711],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9414336,0.0001144222,0.00002052378,0.05691348,0.00007973062,0.0005306174,0.0003846404,0.00003223731,0.0004907256],"genre_scores_gemma":[0.9937,0.00008476312,0.00001408214,0.005930399,0.0001645019,0.000007938971,0.00005406714,0.000004530941,0.00003971369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1799537,"threshold_uncertainty_score":0.981822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2255464129475705,"score_gpt":0.393232650644596,"score_spread":0.1676862376970255,"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."}}