{"id":"W4302937696","doi":"10.48550/arxiv.1707.03778","title":"Catching Zika Fever: Application of Crowdsourcing and Machine Learning\\n for Tracking Health Misinformation on Twitter","year":2017,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Misinformation; Crowdsourcing; Rumor; Social media; Zika virus; Public health; Internet privacy; Citizen science; Public relations; Work (physics); Pandemic; Tracking (education); Data science; Political science; Business; Coronavirus disease 2019 (COVID-19); Computer science; Computer security; Medicine; Engineering; Psychology; World Wide Web; Virology","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":[0.001987188,0.0009658691,0.000486602,0.004240606,0.001140332,0.0017229,0.0009093609,0.001398295,0.001475218],"category_scores_gemma":[0.008731337,0.0003030332,0.000726796,0.001633104,0.0004917303,0.001355946,0.002219384,0.0009316457,0.001623314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006973944,"about_ca_system_score_gemma":0.0009836265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01968068,"about_ca_topic_score_gemma":0.02538918,"domain_scores_codex":[0.9980307,0.0007278183,0.00012313,0.0004685602,0.0004644968,0.0001853184],"domain_scores_gemma":[0.9963508,0.002049825,0.0003578056,0.0006003496,0.0003853533,0.0002559597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001304507,0.001327432,0.1672151,0.001469178,0.0007532962,0.002325434,0.005281493,0.03820352,0.0226876,0.005166581,0.0674516,0.6868142],"study_design_scores_gemma":[0.0001289674,0.0003668757,0.06037519,0.0002143184,0.0001516474,0.0003595754,0.004248642,0.8427373,0.01468163,0.01236805,0.06414627,0.0002214702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6802022,0.003865582,0.1939151,0.008138445,0.00197455,0.001982576,0.02251544,0.04638003,0.04102598],"genre_scores_gemma":[0.8652606,0.0006811605,0.1145139,0.0008670194,0.000351573,0.0004097842,0.0102118,0.0003755564,0.007328672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01968068,"threshold_uncertainty_score":0.03913224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1459726044383094,"score_gpt":0.2702168210404187,"score_spread":0.1242442166021093,"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."}}