{"id":"W4293087581","doi":"10.1109/icaccs54159.2022.9785149","title":"Retracted: Spam Detection for Social Media Networks Using Machine Learning","year":2022,"lang":"en","type":"article","venue":"2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS)","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":10,"is_retracted":true,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Spamming; Harm; Internet privacy; Purchasing; Forum spam; Product (mathematics); Computer science; The Internet; Order (exchange); Publishing; Profit (economics); Competition (biology); Social media; Computer security; Spambot; Advertising; Business; World Wide Web; Marketing; Psychology; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002912423,0.0014159,0.001167632,0.005368964,0.001472876,0.001932398,0.001622225,0.002192198,0.002928443],"category_scores_gemma":[0.01118302,0.00048188,0.0009905928,0.001274916,0.0007184292,0.004577246,0.00190735,0.001303126,0.003829704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001289548,"about_ca_system_score_gemma":0.0008925034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005772144,"about_ca_topic_score_gemma":0.004886771,"domain_scores_codex":[0.9979106,0.0007487619,0.0001276832,0.0003998736,0.000603022,0.0002100843],"domain_scores_gemma":[0.9952206,0.001786008,0.0004748329,0.0008004615,0.001439246,0.0002789044],"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":[0.0004670703,0.0009733639,0.03178688,0.0003175527,0.0002163492,0.0006965399,0.0005779527,0.03299843,0.005844072,0.003642049,0.02820824,0.8942715],"study_design_scores_gemma":[0.00002221651,0.0001770997,0.00418347,0.0000329895,0.00002976246,0.0001904222,0.0001803802,0.980914,0.00446196,0.004339803,0.005436723,0.00003119233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2646056,0.004064129,0.661009,0.004035635,0.001587994,0.001636761,0.003073722,0.0446775,0.01530972],"genre_scores_gemma":[0.7966407,0.0008339761,0.18278,0.0008011918,0.0009730475,0.0005316889,0.002851113,0.000428892,0.0141594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9978078,"threshold_uncertainty_score":0.0154025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06392461342195487,"score_gpt":0.3099117296207101,"score_spread":0.2459871161987552,"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."}}