{"id":"W3203157355","doi":"10.48550/arxiv.2110.00737","title":"A Survey of COVID-19 Misinformation: Datasets, Detection Techniques and Open Issues","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Misinformation; Coronavirus disease 2019 (COVID-19); Computer science; Social media; Data science; Pandemic; Dimension (graph theory); Artificial intelligence; Domain (mathematical analysis); Computer security; World Wide Web; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.007535193,0.001479923,0.001223086,0.02188926,0.00186956,0.003993221,0.00194055,0.002716037,0.002585264],"category_scores_gemma":[0.06083548,0.0004821006,0.0009580398,0.02296708,0.001312096,0.005849348,0.003363373,0.001845613,0.002288071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001785301,"about_ca_system_score_gemma":0.004202555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009543214,"about_ca_topic_score_gemma":0.01214313,"domain_scores_codex":[0.9891909,0.002989781,0.002223157,0.0011017,0.003963729,0.0005306882],"domain_scores_gemma":[0.9306943,0.03867349,0.009703612,0.009093931,0.01043568,0.001399025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0005176377,0.0003147621,0.2199499,0.01256624,0.0005497061,0.0007504071,0.001951492,0.004526969,0.001808071,0.01259971,0.4451591,0.299306],"study_design_scores_gemma":[0.00006449268,0.0002018331,0.1633358,0.006810642,0.000403101,0.002574527,0.007620791,0.01466632,0.00853613,0.01830795,0.7771636,0.0003148324],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1433654,0.1001896,0.03468327,0.02115027,0.002555195,0.001488455,0.639975,0.004912092,0.05168082],"genre_scores_gemma":[0.1779248,0.03897005,0.04157898,0.004699258,0.001516553,0.001312383,0.7289608,0.0004656809,0.004571555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02188926,"threshold_uncertainty_score":0.03985035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2056125230814725,"score_gpt":0.3129308436403093,"score_spread":0.1073183205588368,"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."}}