{"id":"W3096451393","doi":"10.2196/20550","title":"Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach","year":2020,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":384,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bigram; Social distance; Social media; Feeling; Pandemic; Coronavirus disease 2019 (COVID-19); Sentiment analysis; Anger; Psychology; Internet privacy; Public health; Computer science; Artificial intelligence; Social psychology; World Wide Web; Medicine; Trigram","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01087425,0.00005527136,0.0001331776,0.0001046865,0.0004646272,0.0002369445,0.0006249381,0.0001146666,0.003313928],"category_scores_gemma":[0.02250711,0.00002682488,0.00006167647,0.0003228305,0.0007045714,0.0002070169,0.0001968154,0.001902368,0.00003075821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008794572,"about_ca_system_score_gemma":0.0006092637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005647303,"about_ca_topic_score_gemma":0.000125163,"domain_scores_codex":[0.9955649,0.001265182,0.000341822,0.00008048351,0.002468398,0.0002791929],"domain_scores_gemma":[0.9974,0.0007701184,0.0001162094,0.00006119993,0.0001650853,0.001487392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001080421,0.0001042838,0.01019012,0.00006273109,0.00007622269,0.00004227597,0.4440855,0.00005100802,0.00002676623,0.01338501,0.5018429,0.03002508],"study_design_scores_gemma":[0.0005603068,0.0001895388,0.001374402,0.00005255021,0.000007722282,0.0001204673,0.05098506,0.01087782,0.000001537846,0.0003794749,0.9353933,0.00005779725],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1624143,0.000633856,0.01850901,0.7880653,0.0001691657,0.0002285449,0.000002682294,0.00002960982,0.02994755],"genre_scores_gemma":[0.9860834,0.001471286,0.00007174285,0.00777645,0.0005643412,7.907311e-7,0.000001523148,0.000004737674,0.004025765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8236691,"threshold_uncertainty_score":0.9975972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3038354283011451,"score_gpt":0.5007965093349296,"score_spread":0.1969610810337845,"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."}}