{"id":"W4321770261","doi":"10.1371/journal.pone.0277878","title":"Sentiment analysis and causal learning of COVID-19 tweets prior to the rollout of vaccines","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Western University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Causal inference; Coronavirus disease 2019 (COVID-19); Pandemic; Sentiment analysis; Social media; Mood; Public health; Population; Microblogging; Social distance; Demography; Psychology; Medicine; Disease; Artificial intelligence; Social psychology; Sociology; Computer science; Pathology; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003862252,0.00006852159,0.000229677,0.0001675887,0.0001201349,0.0000353238,0.0003547046,0.00001964164,0.000006155697],"category_scores_gemma":[0.0001027854,0.00004598733,0.0000564104,0.001552513,0.00001375953,0.00004640781,0.0005575257,0.00007797562,0.000004701894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008824837,"about_ca_system_score_gemma":0.00002367414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009422667,"about_ca_topic_score_gemma":0.00003473543,"domain_scores_codex":[0.9990497,0.00008534407,0.0002012901,0.0002067268,0.000296072,0.0001608492],"domain_scores_gemma":[0.999234,0.0002456254,0.0001082734,0.000263776,0.00005708062,0.00009126425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004533937,0.0005291589,0.4257799,0.0004497556,0.00277908,0.00004182033,0.008153774,0.5047169,0.04098075,0.0005407571,0.0008716681,0.01511108],"study_design_scores_gemma":[0.0002683965,0.0001542118,0.06436326,0.00006296863,0.0002430356,9.286545e-7,0.00008183481,0.923322,0.0110452,0.0000984699,0.0002424871,0.0001172765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9728589,0.0001004383,0.02220259,0.004585757,0.00002687019,0.0001449577,8.754677e-7,0.00005994875,0.00001962855],"genre_scores_gemma":[0.9967446,0.00002827186,0.00274361,0.0001216674,0.00004401849,0.000004013251,0.000001031243,0.000004029062,0.0003087429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.418605,"threshold_uncertainty_score":0.1875309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04332603795263953,"score_gpt":0.2728551895272322,"score_spread":0.2295291515745927,"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."}}