{"id":"W7115557963","doi":"10.2139/ssrn.5908872","title":"Digital News Consumption: Evidence from Smartphone Content in the 2024 US Elections","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Social Media and Politics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Social media; Content (measure theory); Content analysis; Percentile; Digital media; Media content; User-generated content","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":[0.001608899,0.0003296086,0.0003212928,0.001944683,0.001036071,0.004024532,0.0005252796,0.001431359,0.01591853],"category_scores_gemma":[0.01801823,0.0004261518,0.0004067805,0.004251089,0.0008663916,0.001845612,0.001589573,0.001238245,0.002921328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007005415,"about_ca_system_score_gemma":0.0004516807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03734834,"about_ca_topic_score_gemma":0.0449268,"domain_scores_codex":[0.998264,0.0006758966,0.0001565313,0.0002356669,0.0003364918,0.0003314001],"domain_scores_gemma":[0.9800292,0.007446189,0.008000656,0.001018361,0.002270075,0.001235462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001824147,0.0005436181,0.9491006,0.0003496535,0.0003787918,0.0003658879,0.005287335,0.0001539021,0.0004873617,0.001764864,0.01233014,0.02741363],"study_design_scores_gemma":[0.00003649425,0.000135378,0.9838772,0.00009231244,0.0001625312,0.00007639698,0.006404256,0.0002360905,0.0002818257,0.000295832,0.00838452,0.00001709076],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733441,0.001343592,0.00009208916,0.002325982,0.00009726146,0.00002807873,0.003963369,0.000009085617,0.01879643],"genre_scores_gemma":[0.9927515,0.0009612628,0.00004804497,0.0004387063,0.0001837447,0.00002279861,0.001761573,0.00001521054,0.003817142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03734834,"threshold_uncertainty_score":0.0742619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07391624009952853,"score_gpt":0.3496074788577155,"score_spread":0.275691238758187,"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."}}