{"id":"W2999864926","doi":"10.4230/lipics.icdt.2020.24","title":"Reverse Prevention Sampling for Misinformation Mitigation in Social Networks","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Misinformation; Limiting; Scalability; Set (abstract data type); Computer science; Social network (sociolinguistics); Work (physics); Discrete mathematics; Combinatorics; Theoretical computer science; Algorithm; Mathematics; Social media; Computer security; Physics; Engineering; World Wide Web","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":[0.005753835,0.001874058,0.002193575,0.002073098,0.001663524,0.001823458,0.002981675,0.002368172,0.002327158],"category_scores_gemma":[0.02290817,0.001025409,0.001679669,0.002116392,0.002129579,0.004917272,0.002812258,0.002729879,0.0009504615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001976216,"about_ca_system_score_gemma":0.003263209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003380784,"about_ca_topic_score_gemma":0.004794157,"domain_scores_codex":[0.9941946,0.002972748,0.0002349371,0.001144037,0.001139624,0.0003141156],"domain_scores_gemma":[0.9781065,0.01414523,0.001770596,0.004582926,0.0008832688,0.0005115723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001240254,0.0005538728,0.01123201,0.001039497,0.0005184649,0.0004615251,0.0009935973,0.5561105,0.007752988,0.06416339,0.01479636,0.3411376],"study_design_scores_gemma":[0.00008118348,0.0001101179,0.0007172028,0.00005121521,0.00007398024,0.0002602801,0.000139843,0.9246907,0.00370778,0.06667797,0.00346375,0.00002609269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04313109,0.002007497,0.9463373,0.001896197,0.0001503132,0.0004616563,0.0006608183,0.002380784,0.002974314],"genre_scores_gemma":[0.5293758,0.001045551,0.4625486,0.0008508702,0.0003453774,0.0005374989,0.001620131,0.0002761071,0.003400016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005753835,"threshold_uncertainty_score":0.03042954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1276327980797984,"score_gpt":0.2498521879068814,"score_spread":0.1222193898270831,"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."}}