{"id":"W2919877138","doi":"10.1016/j.ipm.2019.02.016","title":"Detecting breaking news rumors of emerging topics in social media","year":2019,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":255,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Concordia University","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; Saudi Arabian Cultural Mission; Canada Excellence Research Chairs, Government of Canada; King Saud University; National Natural Science Foundation of China; Canada Research Chairs; National Science Foundation","keywords":"Rumor; Computer science; Social media; Fake news; Focus (optics); Task (project management); Statement (logic); Recall; Computer security; Artificial intelligence; Internet privacy; Psychology; Cognitive psychology; World Wide Web; Political science; Public relations; Engineering","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":[0.002238641,0.0006147684,0.0005969637,0.0075694,0.0008497256,0.003060187,0.0005077517,0.001377046,0.001277911],"category_scores_gemma":[0.0173312,0.0003361723,0.0004620022,0.003064304,0.0003055336,0.002849897,0.0009203321,0.001067809,0.0008803366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003997181,"about_ca_system_score_gemma":0.0004191957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003470917,"about_ca_topic_score_gemma":0.004772474,"domain_scores_codex":[0.9986965,0.0003389467,0.0001574866,0.000185737,0.0004812096,0.0001401021],"domain_scores_gemma":[0.9761946,0.01394567,0.004580767,0.001249859,0.003160155,0.0008689473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002121487,0.0008938803,0.7485955,0.0007161201,0.0009278165,0.001419711,0.003279187,0.002764211,0.02792109,0.001685325,0.01174006,0.1979356],"study_design_scores_gemma":[0.00009614235,0.001258491,0.851719,0.0002478209,0.001251081,0.001875786,0.007460197,0.09780642,0.02228974,0.003461938,0.01239387,0.0001395337],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986789,0.001736536,0.005150502,0.0005284059,0.000207307,0.00008905021,0.001566281,0.0002658723,0.003667044],"genre_scores_gemma":[0.992584,0.0004823934,0.003759443,0.00007206554,0.0003473512,0.00002583493,0.001297509,0.00001799957,0.00141335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0075694,"threshold_uncertainty_score":0.01183921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02033234344826333,"score_gpt":0.3055574141943943,"score_spread":0.285225070746131,"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."}}