{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004328584,0.00005812023,0.00008428579,0.00005000722,0.001650314,0.0001652916,0.0001150894,0.0000469087,0.0002395984],"category_scores_gemma":[0.00117448,0.0000608494,0.00002533176,0.0002915876,0.0001232223,0.0002251531,0.00002950828,0.0001398762,0.0000250286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001337052,"about_ca_system_score_gemma":0.0002085367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001211979,"about_ca_topic_score_gemma":0.0008200214,"domain_scores_codex":[0.9992687,0.0001765578,0.0001541881,0.00009865347,0.0001360605,0.0001658981],"domain_scores_gemma":[0.9988915,0.000225206,0.0001016915,0.0001210299,0.00002520266,0.0006354151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002398126,0.00004341796,0.002827223,0.00006499525,0.0000281957,6.613231e-7,0.5125077,0.001525715,0.000008372167,0.1067313,0.3593646,0.0168738],"study_design_scores_gemma":[0.000408255,0.00008182655,0.003652639,0.00005082383,0.000009898108,0.000002392206,0.1344165,0.003401113,0.000007836455,0.0005491099,0.8572385,0.0001811641],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2629167,0.002942876,0.06148174,0.6053994,0.0008501742,0.001315707,0.00002936499,0.001074091,0.06398996],"genre_scores_gemma":[0.9752021,0.000917938,0.00007544125,0.02336389,0.00008448508,0.000003129328,0.0000188771,0.000005083571,0.0003290964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7122853,"threshold_uncertainty_score":0.9996494,"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."}}