{"id":"W4220783355","doi":"10.21203/rs.3.rs-1428343/v1","title":"Detecting Bots in Social-Networks Using Node and Structural Embeddings","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Node (physics); Anonymity; Embedding; Noise (video); Identity (music); Stability (learning theory); Social network (sociolinguistics); Predictive power; Machine learning; Artificial intelligence; Data mining; Social media; World Wide Web; Computer security; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002636004,0.0002028489,0.0002718504,0.0006347257,0.001120696,0.0008781814,0.0009458526,0.0002779561,0.00003592229],"category_scores_gemma":[0.000262834,0.0002246402,0.00008195166,0.001019093,0.00008195191,0.0002936544,0.004914078,0.003198955,0.000001349638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006373601,"about_ca_system_score_gemma":0.0001959234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001531731,"about_ca_topic_score_gemma":0.0001170932,"domain_scores_codex":[0.9964927,0.0007683937,0.0002751566,0.0008351608,0.0009281076,0.0007004273],"domain_scores_gemma":[0.9988128,0.0003461323,0.0001170429,0.0004695272,0.0001517727,0.0001027041],"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":[0.0002626572,0.0001456016,0.1688305,0.003463058,0.0002158161,0.0008550445,0.05202283,0.4607714,0.003913431,0.008562769,0.001020667,0.2999362],"study_design_scores_gemma":[0.0002052336,0.00005140763,0.02845926,0.0002065377,0.00000310881,0.0000207643,0.0004201595,0.9612144,0.0001003439,0.008860316,0.0001663862,0.0002921465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9673249,0.0008731823,0.0296784,0.0002999826,0.0008092986,0.0005080265,0.000006728579,0.0001583576,0.0003411913],"genre_scores_gemma":[0.9956679,0.00004045738,0.003722399,0.00002367344,0.0004291766,0.00004965635,0.00000645483,0.00002597956,0.00003432192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.500443,"threshold_uncertainty_score":0.9991007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08780676378529129,"score_gpt":0.4098511914020843,"score_spread":0.322044427616793,"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."}}