{"id":"W2516338333","doi":"10.18653/v1/w16-0802","title":"Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News","year":2016,"lang":"en","type":"article","venue":"","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":584,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Social Sciences and Humanities Research Council of Canada; Government of Canada","keywords":"Fake news; Computer science; Post truth; Ground truth; Artificial intelligence; Internet privacy; Political science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002844596,0.0007013688,0.000776821,0.003337729,0.0006379821,0.003416553,0.0006349924,0.00119335,0.002024533],"category_scores_gemma":[0.02463202,0.0002392387,0.0004571917,0.0009971504,0.0006446326,0.003036162,0.0009221085,0.001079591,0.001455209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005790645,"about_ca_system_score_gemma":0.0004530049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001061686,"about_ca_topic_score_gemma":0.001716812,"domain_scores_codex":[0.9985415,0.0005011841,0.0001098609,0.0002618021,0.0004332843,0.0001523561],"domain_scores_gemma":[0.983706,0.01011788,0.002397144,0.001271975,0.002108286,0.0003986865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003371189,0.0005205441,0.1439243,0.0008255987,0.0002577008,0.001008259,0.002458979,0.009017941,0.05187184,0.005982848,0.006272288,0.7744885],"study_design_scores_gemma":[0.0001308824,0.001433635,0.2070414,0.0004476184,0.0003767628,0.002701573,0.004789655,0.6548101,0.1012854,0.01247426,0.01426157,0.0002470862],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8841743,0.001434549,0.09830615,0.001355267,0.0002166633,0.0002109567,0.0005430363,0.001467638,0.01229145],"genre_scores_gemma":[0.9587765,0.0002612196,0.03887672,0.0001225804,0.0001212968,0.00002614592,0.0003485582,0.00003896452,0.001427952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003416553,"threshold_uncertainty_score":0.01504385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08927836460139452,"score_gpt":0.3689438054279067,"score_spread":0.2796654408265121,"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."}}