{"id":"W4205231996","doi":"10.2139/ssrn.4001976","title":"A Cross-Country Analysis of Macroeconomic Responses to COVID-19 Pandemic Using Twitter Sentiments","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); York University","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Sentiment analysis; Business; Virology; Computer science; Medicine; Outbreak; Infectious disease (medical specialty); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003672061,0.0001594487,0.0003836474,0.0013178,0.0006263896,0.0002155892,0.001072859,0.00003182574,0.0004010611],"category_scores_gemma":[0.00005226823,0.0001630885,0.0003551017,0.002009115,0.00003435056,0.0002948013,0.000468326,0.0006890607,0.000009413126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002324062,"about_ca_system_score_gemma":0.00215437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002273644,"about_ca_topic_score_gemma":0.0001354126,"domain_scores_codex":[0.9969242,0.00026936,0.0005966668,0.0004116638,0.0004889846,0.001309078],"domain_scores_gemma":[0.9987893,0.0001051378,0.0004522529,0.0004120698,0.00006144783,0.0001797567],"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":[0.0002053046,0.0001175068,0.8146638,0.000004657028,0.004203578,0.00001573837,0.001603635,0.1670824,0.003367859,0.006606845,0.0001363281,0.001992317],"study_design_scores_gemma":[0.005918335,0.001473593,0.0671531,0.00002968044,0.003879018,0.002344732,0.008839558,0.8519304,0.0007078765,0.03556282,0.01994075,0.002220119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8081956,0.000446635,0.1908086,0.0002447061,0.0001809827,0.00006814509,0.000007582122,0.00002176329,0.00002597694],"genre_scores_gemma":[0.9964737,0.00009672511,0.001011709,0.00114835,0.00005877833,0.000005984159,0.000007957234,0.00001173796,0.001185112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7475107,"threshold_uncertainty_score":0.6650556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0416779350523182,"score_gpt":0.3558782409164926,"score_spread":0.3142003058641744,"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."}}