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Register a new userOn Unifying Misinformation Detection
في توحيد الكشف عن المعلومات الخاطئة
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في هذه الورقة، نقدم Unifiedm2، وهو نموذج معلومات فعال للأغراض العامة نماذج مشتركة مجالات متعددة من المعلومات الخاطئة مع إعداد واحد موحد.يتم تدريب النموذج على التعامل مع أربع مهام: اكتشاف تحيز الأخبار، Clicbait، أخبار وهمية، والتحقق من الشائعات.من خلال تجميع هذه المهام معا، يتعلم Unifiedm2 تمثيلا أكثر ثراء من المعلومات الخاطئة، مما يؤدي إلى أداء أحدث أو أداء مماثل في جميع المهام.علاوة على ذلك، نوضح أن التمثيل الموحد الذي تم تعلمه هو مفيد لعدد قليل من التعلم لمهام / مجموعات البيانات / مجموعات التضليلات غير المرئية وتعميمات النموذج للأحداث غير المرئية.
In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news, and verifying rumors. By grouping these tasks together, UnifiedM2 learns a richer representation of misinformation, which leads to state-of-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UnifiedM2's learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets and the model's generalizability to unseen events.
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In this paper we introduce ArCOV19-Rumors, an Arabic COVID-19 Twitter dataset for misinformation detection composed of tweets containing claims from 27th January till the end of April 2020. We collected 138 verified claims, mostly from popular fact-c
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