{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00166232,0.0001404472,0.0002697193,0.0001911542,0.0003663749,0.0003528582,0.0006714875,0.000263112,0.0003150445],"category_scores_gemma":[0.0008116786,0.0001652354,0.00004419668,0.000528505,0.0002401678,0.001364125,0.0009596707,0.0002085464,0.000007323354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002110248,"about_ca_system_score_gemma":0.0009398231,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05867713,"about_ca_topic_score_gemma":0.02209487,"domain_scores_codex":[0.9987781,0.0004169369,0.0002441533,0.0002638842,0.0001258473,0.0001710772],"domain_scores_gemma":[0.9985602,0.0001276026,0.0003840058,0.000408773,0.0002487121,0.0002707191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002163426,0.001147748,0.0353492,0.006090782,0.001472784,0.0005502529,0.4656284,0.01726503,0.0001676026,0.2030456,0.116665,0.1504542],"study_design_scores_gemma":[0.004083804,0.0004173,0.04343271,0.0009617928,0.0004376574,0.00003159805,0.1679586,0.02445178,0.004782118,0.01169881,0.7384799,0.003264012],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8642587,0.0007200077,0.04507042,0.001262929,0.0007344467,0.003163921,0.00245627,0.0005239968,0.08180933],"genre_scores_gemma":[0.9956912,0.002275948,0.0001867557,0.0001747324,0.00002843934,3.583898e-7,0.0006768913,0.000005658861,0.0009600232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6218148,"threshold_uncertainty_score":0.9957494,"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."}}