{"id":"W4394856705","doi":"10.2196/preprints.59425","title":"Long COVID Discourse in Canada, the United States, and Europe: Topic Modeling and Sentiment Analysis of Twitter Data (Preprint)","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Timeline; Topic model; Coronavirus disease 2019 (COVID-19); Narrative; Tracking (education); Sentiment analysis; Perception; Public relations; Political science; History; Sociology; Psychology; Computer science; World Wide Web; Medicine; Linguistics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001485559,0.0004594235,0.0003475655,0.002036323,0.001408301,0.002132987,0.0004551178,0.0004368542,0.001006762],"category_scores_gemma":[0.004470599,0.0001558581,0.0006303498,0.003120486,0.0005824433,0.0007651239,0.0007306168,0.0006709878,0.0003916628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006130455,"about_ca_system_score_gemma":0.005248064,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8068992,"about_ca_topic_score_gemma":0.8162266,"domain_scores_codex":[0.9994741,0.0001371489,0.0000301509,0.00009497782,0.0001436555,0.0001199379],"domain_scores_gemma":[0.997475,0.001468851,0.0001846284,0.00007256236,0.0006568533,0.0001421114],"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":[0.001064828,0.0003884727,0.693161,0.0009070112,0.0004851308,0.001266325,0.04298727,0.03287719,0.009224257,0.009105138,0.08149909,0.1270344],"study_design_scores_gemma":[0.0000460543,0.00007525542,0.5610582,0.0002452824,0.0001817516,0.0001596657,0.05107369,0.327488,0.003765293,0.002820244,0.05292295,0.0001637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9668505,0.0006841134,0.004820631,0.002263366,0.00009317495,0.0001696162,0.01984357,0.0002668773,0.005008124],"genre_scores_gemma":[0.9673519,0.0006222033,0.006986883,0.0002087223,0.00008227395,0.0001657936,0.02155533,0.0000744167,0.002952497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1931008,"threshold_uncertainty_score":0.3884759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05456290852496999,"score_gpt":0.310789079491379,"score_spread":0.256226170966409,"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."}}