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Register a new userUL2C: Mapping User Locations to Countries on Arabic Twitter
UL2C: رسم الخرائط مواقع المستخدمين إلى دول على Twitter العربي
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يمكن أن يكون تعيين مواقع المستخدمين إلى البلدان مفيدا للعديد من التطبيقات مثل تحديد الهدوء ومجموعات المؤلف ونظام التوصية وما إلى ذلك. يسمح Twitter للمستخدمين بإعلان مواقعهم كنصا مجانيا، وغالبا ما تكون هذه المواقع المعلنة من المستخدم صاخبة وصعبة للغاية.في هذه الورقة، نقدم أكبر مجموعة بيانات المسمى يدويا لعودة مواقع المستخدمين على Twitter العربي إلى بلدانهم المقابلة.نبني نماذج تعليمية فعالة من الآلات التي يمكنها أتمتة هذا التعيين كفاءة أفضل بكثير مقارنة بمكتبات مثل Geopy.نظهر أيضا أن DataSet لدينا أكثر فعالية من البيانات المستخرجة من قاعدة بيانات Geonames الجغرافية في هذه المهمة حيث يغطي الأخير المواقع المكتوبة بطرق رسمية فقط.
Mapping user locations to countries can be useful for many applications such as dialect identification, author profiling, recommendation system, etc. Twitter allows users to declare their locations as free text, and these user-declared locations are often noisy and hard to decipher automatically. In this paper, we present the largest manually labeled dataset for mapping user locations on Arabic Twitter to their corresponding countries. We build effective machine learning models that can automate this mapping with significantly better efficiency compared to libraries such as geopy. We also show that our dataset is more effective than data extracted from GeoNames geographical database in this task as the latter covers only locations written in formal ways.
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