{"id":"W4252322379","doi":"10.2196/preprints.31219","title":"Public Attitudes during the Second Lockdown: Sentiment and Topic Analyses using Tweets from Ontario, Canada (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Latent Dirichlet allocation; Sentiment analysis; Government (linguistics); Coronavirus disease 2019 (COVID-19); Topic model; Politics; Public opinion; Public health; Pandemic; Psychological intervention; Political science; Psychology; Medicine; Computer science; Artificial intelligence; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005826384,0.0003017744,0.000255902,0.001717585,0.002088011,0.001688292,0.0004161655,0.0003839896,0.003012487],"category_scores_gemma":[0.003210099,0.0001755357,0.0004471517,0.004333076,0.0004743279,0.0005043152,0.0007084568,0.0004724755,0.0007923009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01596762,"about_ca_system_score_gemma":0.02052161,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9883363,"about_ca_topic_score_gemma":0.9927286,"domain_scores_codex":[0.9994819,0.00003949426,0.00002600524,0.00007814019,0.0002197917,0.0001547201],"domain_scores_gemma":[0.9968141,0.0005309607,0.0002952974,0.00008019224,0.001903168,0.0003762819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007326812,0.00008971628,0.7486283,0.0006716236,0.0001569787,0.0005807633,0.02570528,0.001434871,0.004894792,0.001235517,0.1648994,0.05097006],"study_design_scores_gemma":[0.00001809237,0.00002458871,0.924706,0.0001429231,0.00006259367,0.00005297968,0.01891446,0.002509741,0.000747853,0.0001032548,0.05266454,0.0000531104],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7903516,0.0009020078,0.0008324557,0.003628858,0.0001845744,0.0002493507,0.184202,0.0001367058,0.01951253],"genre_scores_gemma":[0.8868175,0.001271376,0.001754883,0.0006611709,0.0001198222,0.0002944457,0.0882027,0.00009180541,0.02078637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01596762,"threshold_uncertainty_score":0.1158537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1142461490732067,"score_gpt":0.3397858424145198,"score_spread":0.2255396933413131,"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."}}