{"id":"W3096311683","doi":"10.2196/24125","title":"Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Timeline; Pandemic; Public health; Sentiment analysis; Content analysis; Seriousness; Internet privacy; Psychology; Coronavirus disease 2019 (COVID-19); Advertising; Geography; World Wide Web; Computer science; Political science; Medicine; Sociology; Business; Social science","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":[0.002650082,0.0001083283,0.0003379087,0.00005798168,0.001072729,0.0001653102,0.0002949311,0.00007080335,0.00009837911],"category_scores_gemma":[0.003392317,0.00006674961,0.0001034543,0.0008566371,0.0002967803,0.0003022236,0.00006656774,0.0001996337,0.000002749577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001221087,"about_ca_system_score_gemma":0.0008407297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004411402,"about_ca_topic_score_gemma":0.005719206,"domain_scores_codex":[0.9968001,0.001644284,0.0004495355,0.0001738957,0.0005365704,0.0003955857],"domain_scores_gemma":[0.9978818,0.0007106416,0.000490471,0.0001294199,0.0001055188,0.0006821881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00002364289,0.000008118959,0.306268,0.00003608186,0.00005039131,1.351615e-7,0.6910372,2.598581e-7,0.00002571745,0.0006840453,0.000250475,0.001615837],"study_design_scores_gemma":[0.0003110007,0.00001939804,0.8804846,0.000002412644,0.000001525828,1.564445e-7,0.1026198,0.00001776108,6.499631e-7,0.00003016987,0.01643501,0.00007746634],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8640274,0.0001614107,0.00005472759,0.134764,0.00008312309,0.0003018523,0.0002356852,0.0000425015,0.0003293518],"genre_scores_gemma":[0.9880241,0.0002284338,0.000007368236,0.01144866,0.0001828693,0.000007085805,0.00002997273,0.000004863702,0.00006660881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5884175,"threshold_uncertainty_score":0.8250669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1793530029637499,"score_gpt":0.4036931831122839,"score_spread":0.224340180148534,"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."}}