{"id":"W4394973348","doi":"10.32388/ml764l","title":"Review of: \"Information Technology for Detecting Fakes and Propaganda Based on Machine Learning and Sentiment Analysis\"","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Sentiment analysis; Computer science; Artificial intelligence; Data science; Information retrieval; Natural language processing; 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":[0.003108331,0.0009950551,0.001849683,0.007520661,0.001022734,0.003849943,0.002163676,0.003291787,0.02823793],"category_scores_gemma":[0.01983225,0.0004436114,0.001016818,0.007517277,0.001707216,0.003861981,0.0015098,0.002481332,0.01605444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002112319,"about_ca_system_score_gemma":0.005069023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003793955,"about_ca_topic_score_gemma":0.005362663,"domain_scores_codex":[0.9971199,0.0007307809,0.0003959894,0.0003319111,0.001316209,0.0001052736],"domain_scores_gemma":[0.9811921,0.007839638,0.00137063,0.0004636502,0.008505874,0.0006281825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002547472,0.00002226598,0.0001838617,0.01150057,0.0000487769,0.00007269429,0.0001105097,0.0001407849,0.0003011549,0.00371333,0.7772275,0.206653],"study_design_scores_gemma":[0.000006118131,0.00002712507,0.0008246581,0.008115059,0.00004905236,0.000176274,0.00009970417,0.0001451685,0.0001612986,0.001308674,0.9890659,0.00002093015],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008054768,0.7468454,0.005946867,0.1087639,0.08499688,0.0004789556,0.002510402,0.0005380727,0.04911404],"genre_scores_gemma":[0.006847769,0.8623336,0.002941266,0.04373292,0.04039345,0.0004043092,0.002915117,0.0002215335,0.04021002],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02823793,"threshold_uncertainty_score":0.09446532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02447156417352279,"score_gpt":0.3549562440933786,"score_spread":0.3304846799198558,"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."}}