{"id":"W4385785343","doi":"10.25236/ijfs.2023.050811","title":"Social Media Companies’ Moderation of UGC and Journalistic Content Published on Their Platforms","year":2023,"lang":"en","type":"article","venue":"International Journal of Frontiers in Sociology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Vetting; Moderation; Social media optimization; Advertising; Quarter (Canadian coin); User-generated content; Political science; Media studies; Internet privacy; Sociology; Business; History; Psychology; Computer science; Law; Social psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.004554607,0.0002389571,0.0002712945,0.002999405,0.001965288,0.004363965,0.0003119129,0.0008200075,0.005523237],"category_scores_gemma":[0.04290624,0.0002195291,0.0001577836,0.003677116,0.001441256,0.002850618,0.002318209,0.001424445,0.001034899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001170779,"about_ca_system_score_gemma":0.0005623118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003121214,"about_ca_topic_score_gemma":0.004887703,"domain_scores_codex":[0.9953778,0.002296131,0.0003006775,0.0004203077,0.001218178,0.0003869498],"domain_scores_gemma":[0.8968825,0.06093724,0.02961466,0.002908807,0.006298043,0.00335873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00102093,0.0003736707,0.8063751,0.0005281255,0.000231638,0.0008888853,0.1123448,0.0001926521,0.005897734,0.007457483,0.01311014,0.05157869],"study_design_scores_gemma":[0.00002249999,0.0001562612,0.9460214,0.000106787,0.00005384091,0.0001804412,0.02854264,0.0006769254,0.001708962,0.0007227333,0.0217698,0.00003771689],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716482,0.0003572027,0.000252665,0.002103632,0.0001302266,0.00004658942,0.0007237506,0.00003157073,0.0247062],"genre_scores_gemma":[0.9967945,0.0001480329,0.0001360298,0.0002003075,0.0001970401,0.00003372882,0.0002214121,0.00002103985,0.002248011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005523237,"threshold_uncertainty_score":0.02408731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04757185726027149,"score_gpt":0.2785601766416663,"score_spread":0.2309883193813948,"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."}}