{"id":"W3046519084","doi":"10.3390/make2030011","title":"Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter","year":2020,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Immigration; Computer science; Word (group theory); Character (mathematics); Task (project management); Voice activity detection; Recall; Precision and recall; Internet privacy; Artificial intelligence; World Wide Web; Speech processing; Political science; Psychology; Linguistics; Law; Cognitive psychology; Engineering","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":[0.0004567117,0.0003836266,0.0002714084,0.00173141,0.0005141922,0.0005988006,0.0002503306,0.0005881388,0.0007505314],"category_scores_gemma":[0.002574203,0.0001010628,0.0001920787,0.001005074,0.0001918075,0.0007667475,0.0004545232,0.0003752649,0.0009909832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004037927,"about_ca_system_score_gemma":0.0002738128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0106958,"about_ca_topic_score_gemma":0.0236473,"domain_scores_codex":[0.9994414,0.0001801801,0.0000519301,0.0001236307,0.0001183735,0.00008438368],"domain_scores_gemma":[0.9980627,0.0008125586,0.0004016346,0.0001781449,0.0003781953,0.0001666789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000859484,0.0004194897,0.7605134,0.0005471206,0.0001338486,0.001016588,0.00385935,0.003769629,0.03649299,0.0008444645,0.02099999,0.1705437],"study_design_scores_gemma":[0.00002598459,0.0003422024,0.8370907,0.000126267,0.0001096704,0.0009104105,0.006061635,0.1009433,0.03041789,0.0008257451,0.02303611,0.0001100898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846311,0.0001981276,0.002036361,0.0004496035,0.00005417782,0.00006917181,0.007943198,0.0004305525,0.004187705],"genre_scores_gemma":[0.9833006,0.0001750781,0.006074634,0.0001032327,0.0000627257,0.00007768805,0.00803593,0.00002387244,0.002146304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0106958,"threshold_uncertainty_score":0.02126712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02308940963608441,"score_gpt":0.2834093222664004,"score_spread":0.260319912630316,"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."}}