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Register a new userKawarith: an Arabic Twitter Corpus for Crisis Events
Kawarith: كوربوس تويتر العربي لأحداث الأزمات
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توفر منصات الوسائط الاجتماعية (SM) مثل Twitter كميات كبيرة من البيانات في الوقت الفعلي والتي يمكن الاستفادة منها أثناء حالات الطوارئ الجماعية. تتطلب تطوير أدوات لدعم المجتمعات المتأثرة بالأزمات مجموعات البيانات المتاحة، والتي غالبا ما تكون موجودة لغات الموارد المنخفضة. تقدم هذه الورقة Kawarith A Corpus عربي لهي تيتر من أجل أحداث الأزمات، تضم أكثر من مليون تغريدات عربية تم جمعها خلال 22 أزمات حدثت بين عامي 2018 و 2020 وشمل عدة أنواع من الخطر. كشف استكشاف هذا المحتوى عن أهم المواضيع وأنواع المعلومات، وتقدم الورقة مجموعة بيانات معدنية من سبعة أحداث طارئة تعمل كمعيار ذهبي للعديد من المهام في أبحاث المعلوماتية للأزمات. استخدام البيانات المشروحة من نفس الحدث، يكون نموذج BERT يتم ضبطه جيدا لتصنيف تغريدات إلى فئات مختلفة في الإعداد متعدد الملصقات. تظهر النتائج أن النماذج القائمة على بيرت تسفر عن أداء جيد في هذه المهمة حتى مع كميات صغيرة من بيانات التدريب الخاصة بمهام المهام.
Social media (SM) platforms such as Twitter provide large quantities of real-time data that can be leveraged during mass emergencies. Developing tools to support crisis-affected communities requires available datasets, which often do not exist for low resource languages. This paper introduces Kawarith a multi-dialect Arabic Twitter corpus for crisis events, comprising more than a million Arabic tweets collected during 22 crises that occurred between 2018 and 2020 and involved several types of hazard. Exploration of this content revealed the most discussed topics and information types, and the paper presents a labelled dataset from seven emergency events that serves as a gold standard for several tasks in crisis informatics research. Using annotated data from the same event, a BERT model is fine-tuned to classify tweets into different categories in the multi- label setting. Results show that BERT-based models yield good performance on this task even with small amounts of task-specific training data.
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