{"id":"W3171456393","doi":"10.1177/20539517211021115","title":"Studying the COVID-19 infodemic at scale","year":2021,"lang":"en","type":"article","venue":"Big Data & Society","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Canadian Institutes of Health Research","keywords":"Big data; Coronavirus disease 2019 (COVID-19); Data science; Theme (computing); Social media; 2019-20 coronavirus outbreak; Scale (ratio); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computer science; Sociology; Medicine; Data mining; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001687675,0.00006803259,0.00008494061,0.000005967243,0.001580654,0.0001925776,0.0006770418,0.00007592385,0.0006224992],"category_scores_gemma":[0.001329532,0.00005057076,0.00006487414,0.0003541282,0.0002242645,0.0004876447,0.0006390446,0.0001271596,0.0001969749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002407057,"about_ca_system_score_gemma":0.0009653475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008582893,"about_ca_topic_score_gemma":0.00427131,"domain_scores_codex":[0.9987992,0.0001411432,0.0001661678,0.0001656685,0.0004677731,0.0002600489],"domain_scores_gemma":[0.9987444,0.0002236186,0.00007850368,0.0006740647,0.00006073725,0.0002186584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001484812,0.00001370865,0.001186088,0.000009781306,0.00001585842,6.132991e-7,0.1603672,0.000004407498,0.00005731263,0.0004348145,0.8310223,0.006886511],"study_design_scores_gemma":[0.0001800981,0.000002182114,0.001255807,0.000003490581,0.00001042303,0.000002345872,0.08614887,0.0001220961,0.00002237211,0.00007294427,0.9121066,0.00007282748],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6319869,0.00327053,0.006060522,0.1537851,0.003662532,0.001190629,0.001402116,0.0006872274,0.1979545],"genre_scores_gemma":[0.8582286,0.005469962,0.001212053,0.11178,0.001512261,0.000005868691,0.001007567,0.00001942394,0.02076428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2262417,"threshold_uncertainty_score":0.9997191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3383736219038622,"score_gpt":0.4081952060647528,"score_spread":0.06982158416089068,"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."}}