{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003107304,0.0001332455,0.0002179928,0.00008714249,0.002328906,0.00004857316,0.0006367323,0.00007900214,0.0002063985],"category_scores_gemma":[0.0004307802,0.00007880708,0.0001743617,0.0003245237,0.0002670044,0.00005657238,0.0002989444,0.0005404957,0.000004809945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002697575,"about_ca_system_score_gemma":0.000217766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001137862,"about_ca_topic_score_gemma":0.0002741476,"domain_scores_codex":[0.9961415,0.002523171,0.000314982,0.000305721,0.0003619442,0.0003526921],"domain_scores_gemma":[0.9973103,0.002138826,0.0001526787,0.0002802164,0.00003521606,0.00008276146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004447867,0.00003359903,0.09787775,0.000007012734,0.00003389178,0.000001291685,0.01185001,0.8764486,0.000005909808,0.01280085,0.000346938,0.0005496753],"study_design_scores_gemma":[0.0007633687,0.00005579906,0.01850675,0.000005734802,0.00007602406,0.00001700928,0.0135662,0.8364013,0.000001141873,0.03199094,0.09824573,0.0003700169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.88192,0.00007914425,0.07958107,0.03323548,0.000278581,0.0004941625,0.000004548865,0.00009736259,0.004309651],"genre_scores_gemma":[0.9790441,0.000005575187,0.0008052975,0.01827356,0.0003272327,0.0003792984,0.00001120295,0.000008970461,0.001144734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09789878,"threshold_uncertainty_score":0.9989699,"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."}}