{"id":"W4248589786","doi":"10.32920/ryerson.14665035","title":"It's all about you : personalized Facebook news feeds' impact on users' exposure to ideologically varied content","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Social Media and Politics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Professional Engineers Ontario; University of King's College","funders":"","keywords":"Ideology; Limiting; Social media; Internet privacy; Content analysis; Strengths and weaknesses; Content (measure theory); Advertising; Computer science; Public relations; Psychology; Political science; Sociology; World Wide Web; Business; Social psychology; Social science; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.005987588,0.0002967126,0.0004094878,0.002206854,0.001670465,0.006983992,0.000419945,0.0009421285,0.01128911],"category_scores_gemma":[0.04477539,0.0002271252,0.0005038242,0.002286549,0.001815406,0.004810741,0.002709402,0.001578897,0.001152135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001134328,"about_ca_system_score_gemma":0.001053406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001749494,"about_ca_topic_score_gemma":0.003348186,"domain_scores_codex":[0.9951288,0.00290248,0.0002697409,0.0004015766,0.0009623539,0.0003351165],"domain_scores_gemma":[0.9106762,0.074079,0.007534916,0.002328137,0.004212235,0.001169547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001590105,0.0006563677,0.3979277,0.004276929,0.0005746139,0.0007168174,0.2812242,0.000397165,0.004447388,0.02291928,0.009395965,0.2758736],"study_design_scores_gemma":[0.00006068221,0.0007511873,0.6226087,0.002451496,0.0007097385,0.0003470086,0.3055264,0.001019845,0.004240274,0.00874857,0.05340149,0.0001344591],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9576563,0.001682032,0.001146003,0.00324292,0.0001560058,0.000101182,0.0006792396,0.0000410603,0.03529533],"genre_scores_gemma":[0.9949288,0.001080198,0.00103549,0.0004784328,0.0001118658,0.0001275802,0.0001834763,0.00003128909,0.002022804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01128911,"threshold_uncertainty_score":0.03776586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1129074338057738,"score_gpt":0.3754758773882496,"score_spread":0.2625684435824758,"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."}}