{"id":"W7150969612","doi":"10.1109/icaccs54159.2022.11475101","title":"Retraction Notice: Spam Detection for Social Media Networks Using Machine Learning","year":2022,"lang":"","type":"article","venue":"2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS)","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Spambot; The Internet; Support vector machine; Forum spam","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002665507,0.0004786653,0.0005509136,0.0005175166,0.004950349,0.001133415,0.001675086,0.0002908438,0.00007984057],"category_scores_gemma":[0.0004035256,0.0006007242,0.0002034311,0.0008147195,0.0001243796,0.0007026034,0.001225605,0.002522467,0.000005682092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008998448,"about_ca_system_score_gemma":0.0001522159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003053097,"about_ca_topic_score_gemma":0.00006479206,"domain_scores_codex":[0.9945125,0.001644153,0.001143181,0.001041836,0.001127618,0.0005307013],"domain_scores_gemma":[0.9953219,0.001187912,0.001748585,0.0007411707,0.0008460258,0.0001544654],"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":[0.0008589905,0.0003299138,0.0003400595,0.00008920516,0.0002596478,0.000004505848,0.005360538,0.6433955,0.004568167,0.0883285,0.00007434966,0.2563907],"study_design_scores_gemma":[0.001405626,0.0003833499,0.0004500604,0.0002065142,0.00005507092,0.000101022,0.002531454,0.9842907,0.0001400906,0.0008406616,0.009029834,0.0005656012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08882169,0.002556151,0.8851399,0.001529156,0.01864103,0.001287209,0.00009353823,0.0004718406,0.001459533],"genre_scores_gemma":[0.9952353,0.0004826869,0.002472978,0.0001511918,0.000937243,0.0001491985,0.0002053911,0.00005869052,0.0003073127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9064136,"threshold_uncertainty_score":0.9999035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07654506074293362,"score_gpt":0.3260327993896142,"score_spread":0.2494877386466806,"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."}}