{"id":"W3204648175","doi":"10.1109/iccke54056.2021.9721440","title":"Extracting Major Topics of COVID-19 Related Tweets","year":2021,"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":"Concordia University","keywords":"Latent Dirichlet allocation; Topic model; Coronavirus disease 2019 (COVID-19); Social media; Focus (optics); Computer science; Telecommuting; Masking (illustration); Misinformation; Sentiment analysis; Data science; Information retrieval; Artificial intelligence; World Wide Web; Medicine; Disease; Computer security; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0004730084,0.0007382995,0.0003514204,0.006177063,0.0006727214,0.0008685647,0.0002417317,0.0004877513,0.001673699],"category_scores_gemma":[0.001793436,0.0002079035,0.0008047133,0.003592235,0.0001849094,0.0007828264,0.0005842291,0.000422181,0.00117842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004096309,"about_ca_system_score_gemma":0.0006248504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00398077,"about_ca_topic_score_gemma":0.004249732,"domain_scores_codex":[0.9995993,0.00005965834,0.00004926737,0.0001116565,0.00008855704,0.00009163446],"domain_scores_gemma":[0.9991686,0.0003556646,0.0001386122,0.00003917422,0.0002328295,0.00006499704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001537621,0.0005318837,0.2175016,0.002060558,0.0004001538,0.002668214,0.008399468,0.01072622,0.1254629,0.008617738,0.07793777,0.5441558],"study_design_scores_gemma":[0.0001415556,0.0004413167,0.5429122,0.0002952372,0.0005880289,0.002010876,0.01012121,0.2671032,0.04530058,0.01326367,0.1176585,0.0001635243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8497965,0.002257664,0.08322618,0.0009542369,0.0004168862,0.0008927059,0.04576382,0.002387204,0.01430479],"genre_scores_gemma":[0.8788335,0.001386656,0.06843386,0.0001154361,0.000525967,0.0007671372,0.04426672,0.0002033415,0.005467479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006177063,"threshold_uncertainty_score":0.007915199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05215145235084765,"score_gpt":0.3291354493518675,"score_spread":0.2769839970010198,"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."}}