{"id":"W4405166962","doi":"10.2196/53434","title":"Unveiling Topics and Emotions in Arabic Tweets Surrounding the COVID-19 Pandemic: Topic Modeling and Sentiment Analysis Approach","year":2024,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Jordan University of Science and Technology","keywords":"Preprint; Coronavirus disease 2019 (COVID-19); Sentiment analysis; Pandemic; Arabic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Natural language processing; History; Linguistics; Computer science; World Wide Web; Philosophy; Virology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001845567,0.00008021937,0.0001789218,0.0002201716,0.0003121997,0.0001220891,0.00009238296,0.0001289225,0.00003190745],"category_scores_gemma":[0.0003974703,0.00005863201,0.00004539347,0.0004884804,0.0001438668,0.000220919,0.00006280271,0.0002120046,0.00000274559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001397277,"about_ca_system_score_gemma":0.0001264121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007532248,"about_ca_topic_score_gemma":0.0007447361,"domain_scores_codex":[0.9989684,0.0002648098,0.0002598014,0.000167482,0.0001046805,0.0002348659],"domain_scores_gemma":[0.9993812,0.000324683,0.0000430134,0.0001047697,0.0000180366,0.0001282883],"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":[0.000009677524,0.00002773092,0.49817,0.0001462997,0.000178985,0.000003292128,0.2631522,0.08937595,0.00001662259,0.140534,0.0004574842,0.007927691],"study_design_scores_gemma":[0.0002210071,0.00001433883,0.03101376,0.00001539694,0.00008001013,0.00001308833,0.02117575,0.9227169,2.385098e-7,0.006355255,0.01822562,0.0001686418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9672772,0.0004237301,0.02096334,0.006245006,0.0001007426,0.0002358382,0.000002132059,0.00006752257,0.004684524],"genre_scores_gemma":[0.9976451,0.0003347409,0.0002449608,0.001286956,0.0000753924,0.00001089555,0.00000608334,0.000002578412,0.0003933267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8333409,"threshold_uncertainty_score":0.2401219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1500225842334806,"score_gpt":0.4239890442265146,"score_spread":0.2739664599930339,"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."}}