{"id":"W3120968704","doi":"10.21307/connections-2019.018","title":"COVID-19 Health Communication Networks on Twitter: Identifying Sources, Disseminators, and Brokers","year":2020,"lang":"en","type":"article","venue":"Connections","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Misinformation; Credibility; Social media; Information Dissemination; Government (linguistics); Identification (biology); Public health; Public relations; Internet privacy; Coronavirus disease 2019 (COVID-19); Business; Social network analysis; Disease; Political science; Computer science; Medicine; World Wide Web; Computer security; Infectious disease (medical specialty)","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":[0.001953497,0.0002446694,0.0002448469,0.003285385,0.001584098,0.003042858,0.0003929079,0.0006088612,0.003557002],"category_scores_gemma":[0.01090142,0.0002226313,0.0002239139,0.003276961,0.0006181004,0.005263633,0.002088535,0.0005744094,0.0006061075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001015614,"about_ca_system_score_gemma":0.0008856273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007190295,"about_ca_topic_score_gemma":0.009119926,"domain_scores_codex":[0.9987865,0.0005537466,0.0001046729,0.0001695094,0.0001934913,0.0001920967],"domain_scores_gemma":[0.9940363,0.003261579,0.001415878,0.0002192377,0.0006831151,0.0003837187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002624972,0.00008784945,0.7864141,0.0006192191,0.00007284566,0.0008355401,0.1083268,0.0004489618,0.003210338,0.009298839,0.006210599,0.08421251],"study_design_scores_gemma":[0.00002561433,0.0001524968,0.6492982,0.0009019775,0.0001431008,0.0007352191,0.274828,0.01370131,0.002390325,0.008926569,0.0488045,0.00009264814],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835753,0.0007379819,0.002127213,0.002348054,0.00005283011,0.0001194265,0.001007414,0.00003344981,0.009998228],"genre_scores_gemma":[0.9958132,0.000516255,0.00158059,0.000158195,0.00004169894,0.00008519005,0.0004056348,0.00001269508,0.00138656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007190295,"threshold_uncertainty_score":0.01429689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09809080807092531,"score_gpt":0.3887152186822353,"score_spread":0.29062441061131,"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."}}