{"id":"W3162042679","doi":"10.2196/26255","title":"Methodological Clarifications and Generalizing From Weibo Data. Comment on “Nature and Diffusion of COVID-19–related Oral Health Information on Chinese Social Media: Analysis of Tweets on Weibo”","year":2021,"lang":"en","type":"letter","venue":"Journal of Medical Internet Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Social media; Coronavirus disease 2019 (COVID-19); Microblogging; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Internet privacy; Health information; China; Computer science; Psychology; Data science; Sociology; World Wide Web; Political science; Medicine; Health care; Virology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03804776,0.001269599,0.001270728,0.002191689,0.007010544,0.006816985,0.005211328,0.03910129,0.006652655],"category_scores_gemma":[0.2128689,0.001103151,0.002198061,0.002742081,0.007918833,0.006729727,0.003439797,0.05151451,0.007984447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008115653,"about_ca_system_score_gemma":0.00928381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03828678,"about_ca_topic_score_gemma":0.06488689,"domain_scores_codex":[0.9645357,0.01387725,0.004965818,0.003734115,0.01076861,0.002118591],"domain_scores_gemma":[0.7984863,0.1416305,0.00807672,0.005509972,0.04218462,0.004111854],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002181643,0.000008239172,0.0003849817,0.00004716683,0.0000128617,0.0002056723,0.0006899374,0.00002449885,0.0001119925,0.00204701,0.9944916,0.001954249],"study_design_scores_gemma":[0.00009447681,0.00005981785,0.004801251,0.0008540749,0.0000714328,0.0007706676,0.004505621,0.001049825,0.001158864,0.01750191,0.9689233,0.0002088595],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0002799971,0.0002661558,0.0002969344,0.9875822,0.01035836,0.00002938243,0.0002963603,0.00003605495,0.0008547026],"genre_scores_gemma":[0.00208557,0.0001659354,0.0005527519,0.9864933,0.008299709,0.0001146178,0.00006326249,0.00002942666,0.002195428],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9619522,"threshold_uncertainty_score":0.2012182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3554690349088374,"score_gpt":0.5189613256908213,"score_spread":0.1634922907819839,"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."}}