{"id":"W4406800622","doi":"10.31449/inf.v48i4.5977","title":"Exploring the Power of Dual Deep Learning for Fake News Detection","year":2025,"lang":"en","type":"article","venue":"Informatica","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Dual (grammatical number); Fake news; Power (physics); Computer science; Deep learning; Artificial intelligence; Internet privacy; Art; Physics; Literature","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":[0.002361853,0.0007966813,0.0005149367,0.001047345,0.0003268216,0.00128975,0.0008778467,0.0009254294,0.001042373],"category_scores_gemma":[0.007818811,0.0002999013,0.00042005,0.0004278735,0.0006319288,0.002220558,0.001270613,0.001499832,0.0003388817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008059522,"about_ca_system_score_gemma":0.0006932922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00319254,"about_ca_topic_score_gemma":0.002855245,"domain_scores_codex":[0.9994658,0.0001960095,0.00002717602,0.0001209042,0.0001039723,0.00008612251],"domain_scores_gemma":[0.996406,0.002291903,0.0003218742,0.0003915589,0.0004021429,0.0001864381],"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":[0.001615938,0.001069386,0.05555099,0.0002129924,0.0003154026,0.0003254075,0.0004870426,0.3933724,0.0107017,0.01095858,0.003823132,0.5215669],"study_design_scores_gemma":[0.00001012273,0.000075426,0.000949552,0.00000737754,0.00001300521,0.0000245929,0.00003472381,0.9950653,0.001162819,0.002442727,0.0002085715,0.000005735653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6988866,0.001162001,0.2875701,0.001962215,0.0001090458,0.0001075073,0.0003042542,0.001267436,0.008630698],"genre_scores_gemma":[0.9798756,0.0001208673,0.018068,0.0001364212,0.00002705044,0.00001865255,0.000202845,0.00001490965,0.001535578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00319254,"threshold_uncertainty_score":0.01249081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0617939585889917,"score_gpt":0.3174265457077373,"score_spread":0.2556325871187456,"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."}}