{"id":"W3025636516","doi":"10.2196/19447","title":"Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":472,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Medical Research Council; Medical Research Council; Agency for Science, Technology and Research","keywords":"Anger; Sadness; Gratitude; Pandemic; Public health; Psychology; Government (linguistics); Social media; Xenophobia; Health communication; Public opinion; Social psychology; Political science; Public relations; Sociology; Coronavirus disease 2019 (COVID-19); Medicine; Disease; Racism; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0006389862,0.0002674481,0.000253703,0.002541897,0.0006389018,0.001556,0.0002229897,0.0004245047,0.002249304],"category_scores_gemma":[0.004155768,0.0001132897,0.0002527676,0.003635767,0.0003442128,0.002169045,0.001423489,0.0004828458,0.001088748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004742687,"about_ca_system_score_gemma":0.0003286729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003395617,"about_ca_topic_score_gemma":0.005443799,"domain_scores_codex":[0.9994585,0.0001412967,0.00006743044,0.00009212562,0.0001485056,0.00009218116],"domain_scores_gemma":[0.9971322,0.00122679,0.0008479489,0.00008365713,0.0005145855,0.0001947291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005818375,0.00008301133,0.7850195,0.002034239,0.000141802,0.001497156,0.0510261,0.0007649956,0.008130722,0.002558743,0.03253186,0.11563],"study_design_scores_gemma":[0.000009688495,0.000101898,0.8757374,0.0003263949,0.00007047626,0.0005420835,0.06434568,0.003589778,0.001339785,0.0007698393,0.05310373,0.00006326049],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733067,0.000762746,0.0006033311,0.001498063,0.0001333631,0.0001029594,0.01369542,0.00005206406,0.009845471],"genre_scores_gemma":[0.9847258,0.001248315,0.001149914,0.0003233765,0.0002037154,0.0002332963,0.009394127,0.00004314314,0.002678291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003395617,"threshold_uncertainty_score":0.007524669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.151327565391954,"score_gpt":0.4204540639072858,"score_spread":0.2691264985153319,"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."}}