{"id":"W3158589498","doi":"10.18653/v1/2021.nlp4if-1.9","title":"AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; King Abdulaziz City for Science and Technology; Hamad Bin Khalifa University; Compute Canada","keywords":"Disinformation; Computer science; Misinformation; Task (project management); Arabic; Set (abstract data type); Domain (mathematical analysis); Benchmark (surveying); Natural language processing; Artificial intelligence; German; Scale (ratio); Machine learning; Data science; Social media; Computer security; World Wide Web; Linguistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001049548,0.001679074,0.0005696241,0.007053038,0.001314966,0.001456022,0.001240918,0.002210034,0.006699848],"category_scores_gemma":[0.007391743,0.0002635505,0.0008706445,0.004446174,0.0004937683,0.001851465,0.001638458,0.001420561,0.007747102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008860438,"about_ca_system_score_gemma":0.00117533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01508545,"about_ca_topic_score_gemma":0.02291387,"domain_scores_codex":[0.9990636,0.0001868844,0.0001254309,0.0002178146,0.0003095939,0.00009684372],"domain_scores_gemma":[0.9967467,0.00109794,0.0003954995,0.0006580384,0.0008349482,0.0002668406],"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":[0.00159013,0.001152314,0.05562009,0.003498479,0.0004189906,0.00343785,0.001962267,0.00938047,0.01471241,0.004707692,0.7069895,0.1965299],"study_design_scores_gemma":[0.0003656255,0.0003228228,0.1213503,0.0008741639,0.0002256576,0.004682888,0.004039553,0.08680776,0.02124612,0.005929429,0.7539082,0.0002475118],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2849635,0.005708223,0.01556027,0.002546164,0.0009193356,0.000820872,0.6481899,0.01178249,0.02950926],"genre_scores_gemma":[0.1705758,0.0009302329,0.03147909,0.000425684,0.0001855715,0.0004092596,0.7895106,0.0003704137,0.006113264],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01508545,"threshold_uncertainty_score":0.02999532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05182885206336371,"score_gpt":0.3053056938476754,"score_spread":0.2534768417843117,"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."}}