{"id":"W4281680075","doi":"10.1371/journal.pone.0268669","title":"Dynamic topic modeling of twitter data during the COVID-19 pandemic","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; UK Research and Innovation; New York University Shanghai","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Data science; Social media; Computer science; Biology; Medicine; Virology; World Wide Web; Outbreak; Infectious disease (medical specialty)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003894113,0.0007108099,0.0007384539,0.001687498,0.0007277993,0.001718346,0.0008738213,0.001330731,0.001190584],"category_scores_gemma":[0.01025788,0.0003452086,0.0008991731,0.001793161,0.0006253991,0.002164663,0.001167967,0.001673215,0.0008855277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001087757,"about_ca_system_score_gemma":0.0006005184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01567373,"about_ca_topic_score_gemma":0.0190088,"domain_scores_codex":[0.9987293,0.0006525762,0.00006912686,0.0002690147,0.0001106444,0.0001693367],"domain_scores_gemma":[0.9957876,0.003197876,0.0003363575,0.0002461196,0.000276001,0.0001560398],"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":[0.002144089,0.0006367709,0.3664235,0.001301376,0.0005707898,0.001307377,0.007019733,0.3552258,0.008780326,0.04045644,0.0671987,0.1489352],"study_design_scores_gemma":[0.00004915625,0.0000659217,0.03528313,0.00008464941,0.00005247743,0.0001310131,0.001736075,0.9387557,0.0007444998,0.01100816,0.01203653,0.00005280636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8942796,0.004036428,0.06541117,0.009306944,0.0009406069,0.0002462478,0.01943974,0.0006930146,0.005646334],"genre_scores_gemma":[0.9656028,0.001083798,0.01435692,0.000394078,0.0007205806,0.0002147175,0.01465046,0.00009062463,0.002885935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01567373,"threshold_uncertainty_score":0.031165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1691143786469432,"score_gpt":0.3242153130349569,"score_spread":0.1551009343880138,"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."}}