{"id":"W3135897041","doi":"10.2196/27079","title":"Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Social distance; Social media; Government (linguistics); Psychology; China; Coronavirus disease 2019 (COVID-19); Social psychology; Distancing; Logistic regression; Pandemic; Explanatory model; Applied psychology; Geography; Medicine; Computer science; Statistics; Disease","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008477091,0.0003380808,0.0002585164,0.0009674707,0.0006121119,0.0008802363,0.0002195188,0.0003283673,0.001125818],"category_scores_gemma":[0.002925495,0.0001076325,0.0004197501,0.0013126,0.0004854221,0.0005723363,0.0007294508,0.0003671115,0.0001661167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007233139,"about_ca_system_score_gemma":0.0004467619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0178355,"about_ca_topic_score_gemma":0.01844616,"domain_scores_codex":[0.9994397,0.0001845581,0.00005172765,0.00008543329,0.0001144393,0.0001241629],"domain_scores_gemma":[0.9982444,0.0005952594,0.000509217,0.0001185806,0.0002965724,0.0002360104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001430407,0.00009241157,0.9792534,0.00007309402,0.00006090547,0.0002165106,0.008092184,0.0002012909,0.0008932062,0.00009141425,0.0006303518,0.01025207],"study_design_scores_gemma":[0.000002793133,0.00004162687,0.9907559,0.00001113526,0.00002002404,0.00004451891,0.007476744,0.001061249,0.0001559797,0.00003929769,0.0003795,0.0000112008],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992822,0.00002465422,0.00005887386,0.00005778185,0.000003503882,0.00001129709,0.0002016558,0.000002112369,0.0003579389],"genre_scores_gemma":[0.9993105,0.00003513675,0.00006704856,0.00003320059,0.000008102109,0.00002456208,0.0003530912,0.000001191699,0.0001671884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0178355,"threshold_uncertainty_score":0.03546339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06739921183622492,"score_gpt":0.4042278041638236,"score_spread":0.3368285923275987,"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."}}