{"id":"W3080644557","doi":"10.1007/978-3-030-57796-4_25","title":"COVID-19-FAKES: A Twitter (Arabic/English) Dataset for Detecting Misleading Information on COVID-19","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":92,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Misinformation; Computer science; Coronavirus disease 2019 (COVID-19); Sentiment analysis; Social media; Information retrieval; Data science; Artificial intelligence; World Wide Web; Computer security","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":[0.001119302,0.001892805,0.0008333085,0.004704106,0.001470964,0.001224746,0.001086785,0.00212382,0.01033034],"category_scores_gemma":[0.005050626,0.0003438629,0.0005951569,0.002209765,0.000411311,0.002078966,0.002262989,0.001122835,0.01734412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008104831,"about_ca_system_score_gemma":0.001039368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01126712,"about_ca_topic_score_gemma":0.02117213,"domain_scores_codex":[0.998442,0.000241334,0.0002006016,0.0001967497,0.0007304871,0.0001889396],"domain_scores_gemma":[0.9966103,0.0007959995,0.0004445817,0.000874943,0.0009032886,0.0003707944],"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":[0.0005770467,0.0003088331,0.01641474,0.001108184,0.0001025001,0.0005154241,0.0002701238,0.001996291,0.006514275,0.0017932,0.9028028,0.0675965],"study_design_scores_gemma":[0.0003860698,0.0006980016,0.1025285,0.0005423377,0.000191743,0.002544948,0.002051793,0.1001377,0.03327591,0.005929896,0.7513248,0.0003882816],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.09837104,0.001620682,0.009216987,0.001939257,0.001464427,0.0008858134,0.8471392,0.01658799,0.02277456],"genre_scores_gemma":[0.06774091,0.0004268371,0.01830094,0.000495095,0.0002778909,0.0005374866,0.9002665,0.000616327,0.01133798],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01126712,"threshold_uncertainty_score":0.03455848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08081274970583942,"score_gpt":0.3717163613092162,"score_spread":0.2909036116033768,"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."}}