{"id":"W4297536022","doi":"10.1007/s11042-022-13777-0","title":"Net activism and whistleblowing on YouTube: a text mining analysis","year":2022,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Centre National de la Recherche Scientifique; Institut national de la recherche scientifique","keywords":"Computer science; Metadata; Representation (politics); Hierarchy; Natural language processing; Social media; Context (archaeology); Feature (linguistics); Cluster analysis; Artificial intelligence; Information retrieval; Resource (disambiguation); World Wide Web; Linguistics; Politics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001583398,0.00009081444,0.000122479,0.0001557918,0.0006041413,0.0001731151,0.000189799,0.00002442068,0.00002868441],"category_scores_gemma":[0.0000148913,0.00009125642,0.00004265852,0.0007262694,0.00002703831,0.0001652566,0.0001722898,0.0001308271,0.000008843616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002603743,"about_ca_system_score_gemma":0.00001674499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000346844,"about_ca_topic_score_gemma":0.00000693612,"domain_scores_codex":[0.9991634,0.00003632447,0.0001197913,0.0003634713,0.0001617232,0.0001552347],"domain_scores_gemma":[0.9994062,0.0001427366,0.0000518803,0.0002915964,0.00001703319,0.00009053816],"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":[0.000005999495,0.00008750134,0.001191504,0.00000498731,0.0001037602,0.00000321031,0.001523957,0.0006355965,0.003334891,0.002869384,0.0002917669,0.9899474],"study_design_scores_gemma":[0.001182906,0.0003177206,0.06510468,0.00001186852,0.0003193807,0.00004385705,0.001369231,0.680731,0.002615021,0.000829127,0.246658,0.0008172123],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5345002,0.0003101063,0.4566331,0.002333747,0.0001751395,0.0008443377,0.00009051266,0.0003974682,0.004715387],"genre_scores_gemma":[0.9835228,0.00002421818,0.01529158,0.000240594,0.00006120122,0.0004971549,0.00002595067,0.000006936398,0.0003295087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9891303,"threshold_uncertainty_score":0.4646628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01736515410158408,"score_gpt":0.240667287306864,"score_spread":0.2233021332052799,"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."}}