{"id":"W3080274575","doi":"10.1007/s41666-021-00111-w","title":"COVID-19 Pandemic: Identifying Key Issues Using Social Media and Natural Language Processing","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; Dalhousie University","funders":"Dalhousie University; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Compute Canada","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Sentiment analysis; Social media; Thematic analysis; Politics; Perception; Political science; Public relations; Sociology; Psychology; Social science; Qualitative research; Computer science; Medicine; Disease; Natural language processing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003994112,0.0006341366,0.000401174,0.006507271,0.001453333,0.00301927,0.001028137,0.002064061,0.003255605],"category_scores_gemma":[0.01813712,0.0002421622,0.0004909517,0.003117486,0.0009870803,0.004721321,0.002339822,0.00193871,0.001137317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001437942,"about_ca_system_score_gemma":0.003679313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0209088,"about_ca_topic_score_gemma":0.02160784,"domain_scores_codex":[0.9968906,0.001038218,0.0003623763,0.0003289078,0.0009832924,0.0003966292],"domain_scores_gemma":[0.9819302,0.01185569,0.002002506,0.0008826871,0.002442917,0.000885975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001112306,0.001507294,0.4040511,0.002692739,0.0003851996,0.003078674,0.006349597,0.005724799,0.01306953,0.01632842,0.2445064,0.3011939],"study_design_scores_gemma":[0.0002268064,0.001212264,0.4227939,0.00254793,0.000655086,0.003437524,0.08144646,0.1386545,0.02446513,0.03621005,0.2878485,0.0005019405],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7304402,0.004973721,0.01977564,0.07297769,0.002792862,0.003008767,0.1190818,0.002295934,0.04465333],"genre_scores_gemma":[0.8809621,0.002428719,0.03255058,0.006030606,0.001341456,0.0008971563,0.07074086,0.0002040131,0.004844487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0209088,"threshold_uncertainty_score":0.04157418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3688109419020878,"score_gpt":0.5754048449226615,"score_spread":0.2065939030205737,"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."}}