{"id":"W4411686484","doi":"10.2196/58302","title":"Modularity of Online Social Networks and COVID-19 Misinformation Spreading in Russia: Combining Social Network Analysis and National Representative Survey","year":2025,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Misinformation; Social media; Popularity; Social network (sociolinguistics); Modularity (biology); Psychology; Coronavirus disease 2019 (COVID-19); Social distance; Association (psychology); Social psychology; Internet privacy; Political science; Computer science; Medicine; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.00384841,0.00009904894,0.0003948571,0.0003335667,0.0005146551,0.00004097002,0.0001087933,0.0002884621,0.00002249588],"category_scores_gemma":[0.002279698,0.0001038962,0.00005350536,0.001260407,0.0004564766,0.0003611078,0.0001080691,0.0002389025,2.811833e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001762979,"about_ca_system_score_gemma":0.0003822165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004195491,"about_ca_topic_score_gemma":0.0108067,"domain_scores_codex":[0.9979665,0.0008243609,0.0005867059,0.0001440344,0.0001880979,0.0002902909],"domain_scores_gemma":[0.9982298,0.001108438,0.0003632163,0.0000517686,0.0001530343,0.0000937165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001276054,0.0000341777,0.8150209,0.00002765042,0.0001431137,3.114269e-7,0.05142637,0.02737015,0.000001280649,0.1007195,0.00373865,0.001390221],"study_design_scores_gemma":[0.0005342878,0.00001026346,0.9067084,0.000006359549,0.00002451652,2.067788e-7,0.004451681,0.08167962,2.957276e-7,0.006293003,0.0002105976,0.00008073114],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9735082,0.0000489114,0.01626895,0.002838613,0.00008088528,0.0003038379,0.00004729613,0.00003161592,0.006871741],"genre_scores_gemma":[0.9976473,0.00006418908,0.0002289938,0.001745813,0.00007180027,0.000005416659,0.0001852324,0.000002045563,0.00004915816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09442652,"threshold_uncertainty_score":0.6342355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07306999045231481,"score_gpt":0.4431097370338635,"score_spread":0.3700397465815487,"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."}}