{"id":"W2973835677","doi":"10.1109/iri.2019.00066","title":"Fake News Detection Using Bayesian Inference","year":2019,"lang":"en","type":"article","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Multinomial distribution; Markov chain Monte Carlo; Exponential family; Computer science; Dirichlet distribution; Bayesian probability; Bayesian inference; Inference; Artificial intelligence; Mixture model; Machine learning; Gibbs sampling; Algorithm; Data mining; Mathematics; Econometrics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009713198,0.00006936234,0.0000664425,0.0000779672,0.00006844323,0.000170085,0.0002612395,0.00005118585,0.00007444729],"category_scores_gemma":[0.00001866722,0.00006278747,0.00003175734,0.0003052379,0.000006245575,0.0006208245,0.00007222814,0.00008574878,0.0002271653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003679228,"about_ca_system_score_gemma":0.00002515195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006207142,"about_ca_topic_score_gemma":0.0001999405,"domain_scores_codex":[0.9993755,0.00002753551,0.00009830618,0.000227977,0.0001315272,0.0001391044],"domain_scores_gemma":[0.9995186,0.0000338049,0.00003900188,0.0003316496,0.00003240828,0.00004454301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001924661,0.00007410863,0.05077396,0.0000476602,0.00002872926,0.000006511126,0.001237901,0.00454466,0.2240786,0.03618628,0.00009958079,0.6829028],"study_design_scores_gemma":[0.0001948834,0.0001089572,0.007358907,0.00001511471,0.000002880313,0.00002089426,0.00002721059,0.9410224,0.04420124,0.004522643,0.002317212,0.0002076881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2761016,0.000005452022,0.7182156,0.00005772704,0.0007535978,0.00006081061,5.98668e-8,0.0001686717,0.004636377],"genre_scores_gemma":[0.9832788,0.000001498689,0.01608665,0.0001965076,0.00005993477,0.000001352225,1.492293e-7,0.000004087608,0.0003710563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9364777,"threshold_uncertainty_score":0.2919827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01904359245319845,"score_gpt":0.2496959917253967,"score_spread":0.2306523992721983,"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."}}