{"id":"W4207023298","doi":"10.2196/31793","title":"(Mis)Information on Digital Platforms: Quantitative and Qualitative Analysis of Content From Twitter and Sina Weibo in the COVID-19 Pandemic","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Misinformation; Social media; Microblogging; Pandemic; Context (archaeology); Content analysis; Coronavirus disease 2019 (COVID-19); Public health; Disinformation; Internet privacy; Computer science; World Wide Web; Sociology; Geography; Medicine","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":[0.006383582,0.0002620938,0.0002882558,0.002602272,0.001630379,0.002189907,0.0004408611,0.0007335792,0.001914896],"category_scores_gemma":[0.02701322,0.0002190566,0.0002262762,0.00266932,0.00249388,0.003822064,0.002818446,0.0007817682,0.0003446568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002106402,"about_ca_system_score_gemma":0.00130001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004942366,"about_ca_topic_score_gemma":0.006797088,"domain_scores_codex":[0.9948674,0.003172041,0.0002617499,0.000368768,0.0009050093,0.0004249443],"domain_scores_gemma":[0.9706849,0.02166317,0.003395027,0.000571568,0.00306635,0.0006188868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002027021,0.00008370216,0.1592946,0.0006938296,0.00002785726,0.0005042439,0.8069151,0.00017096,0.00358335,0.002315411,0.002421768,0.02378638],"study_design_scores_gemma":[0.000007341478,0.00009048056,0.1409097,0.000365061,0.00002066016,0.0001617751,0.8425236,0.0008214636,0.001293701,0.0008644364,0.01289454,0.00004725069],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931918,0.0001380726,0.0008163809,0.0007602267,0.00002133221,0.0001562523,0.001301787,0.000009167243,0.003605131],"genre_scores_gemma":[0.9958156,0.0002009662,0.001041284,0.0003843663,0.00002819486,0.0005245779,0.0005272889,0.00002314959,0.001454608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006383582,"threshold_uncertainty_score":0.03376001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2948667033179733,"score_gpt":0.4770173383196319,"score_spread":0.1821506350016586,"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."}}