{"id":"W4311241838","doi":"10.2196/41198","title":"Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster–Based BERT Topic Modeling Approach","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of British Columbia; Dalhousie University; Université Laval; Institut National de Santé Publique du Québec; Centre hospitalier universitaire de Québec; Université de Sherbrooke","funders":"","keywords":"Context (archaeology); Social media; Politics; Public health; Political science; Salient; Identity (music); Sociology; Public relations; World Wide Web; Computer science; Geography; Medicine; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.004602019,0.0006005115,0.0005496785,0.003261412,0.001316558,0.00253774,0.001083897,0.001116104,0.002351921],"category_scores_gemma":[0.009358498,0.0004332404,0.0009715845,0.002389272,0.001465353,0.002883516,0.001995296,0.00117704,0.0005053299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002304933,"about_ca_system_score_gemma":0.001169668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02121855,"about_ca_topic_score_gemma":0.02297883,"domain_scores_codex":[0.9978854,0.00131225,0.00005945215,0.0004260123,0.0001502854,0.0001666353],"domain_scores_gemma":[0.9898589,0.008572848,0.0005577046,0.0004207383,0.0003682023,0.0002216568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001852528,0.0007236824,0.417575,0.0009169795,0.0006065606,0.001165768,0.1234322,0.1106948,0.01013143,0.1365514,0.008306542,0.1880433],"study_design_scores_gemma":[0.00002369319,0.0001147567,0.06457206,0.0001065961,0.00009743378,0.0001542474,0.02154806,0.8743093,0.001268906,0.02972624,0.007991703,0.00008693736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8025978,0.0005940673,0.1826074,0.002761494,0.00005716733,0.0004247033,0.002278972,0.0003031966,0.008375064],"genre_scores_gemma":[0.9691874,0.0001350649,0.02826127,0.00006912927,0.00003610694,0.0002180846,0.0009124295,0.0000320425,0.001148438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02121855,"threshold_uncertainty_score":0.04219007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1671066051381052,"score_gpt":0.4412386824298929,"score_spread":0.2741320772917877,"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."}}