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Register a new userArabic Offensive Language on Twitter: Analysis and Experiments
اللغة الهجومية العربية على تويتر: التحليل والتجارب
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الكشف عن اللغة الهجومية على Twitter لديها العديد من التطبيقات التي تتراوح من الكشف / التنبؤ بالتنبؤ لقياس الاستقطاب.في هذه الورقة، نركز على بناء مجموعة بيانات تغريدة عربية كبيرة.نقدم طريقة لبناء مجموعة بيانات غير متحيزة حسب الموضوع أو اللهجة أو الهدف.نحن ننتج أكبر مجموعة بيانات عربية حتى الآن مع علامات خاصة لخطاب الاداءات والكراهية.نحن نحلل تماما مجموعة البيانات لتحديد الموضوعات واللهجات والجنس الأكثر ترتبط أكثر من التغريدات الهجومية وكيفية استخدام المتحدثين باللغة العربية اللغة.أخيرا، نقوم بإجراء العديد من التجارب لإنتاج نتائج قوية (F1 = 83.2) على مجموعة البيانات باستخدام تقنيات SOTA.
Detecting offensive language on Twitter has many applications ranging from detecting/predicting bullying to measuring polarization. In this paper, we focus on building a large Arabic offensive tweet dataset. We introduce a method for building a dataset that is not biased by topic, dialect, or target. We produce the largest Arabic dataset to date with special tags for vulgarity and hate speech. We thoroughly analyze the dataset to determine which topics, dialects, and gender are most associated with offensive tweets and how Arabic speakers useoffensive language. Lastly, we conduct many experiments to produce strong results (F1 =83.2) on the dataset using SOTA techniques.
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