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Register a new userFANG-COVID: A New Large-Scale Benchmark Dataset for Fake News Detection in German
Fang-Covid: مجموعة بيانات قياسية جديدة على نطاق واسع للكشف عن الأخبار وهمية باللغة الألمانية
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مع استمرار العالم في محاربة جائحة CovID-19، فإنه يقاتل في وقت واحد من نقص الدم "- وهو طوفان من تضليل وانتشار نظريات المؤامرة المؤدية إلى تهديدات صحية وشعبة المجتمع. لمكافحة هذا المعكرية، هناك حاجة ملحة لمجموعات البيانات القياسية التي يمكن أن تساعد الباحثين على تطوير وتقييم النماذج الموجهة نحو الكشف التلقائي عن التضليل. في حين أن هناك جهودا متزايدة لإنشاء مجموعات بيانات قياسية كافية ومفتوحة للمصدر للغة الإنجليزية، فإن الموارد القابلة للمقارنة غير متاحة تقريبا بالنسبة للألمانية، مما يترك البحث في اللغة الألمانية متخلفة بشكل كبير. في هذه الورقة، نقدم DataSet المعيار الجديد Fang-Covid يتكون من 28،056 مواد إخبارية ألمانية حقيقية و 13،186 مرتبطة بمعائق CovID-19 وكذلك بيانات عن انتشارها على Twitter. علاوة على ذلك، نقترح نموذجا قابل للتفسير القائم على السياق والاجتماعي للكشف عن الأخبار المزيفة، ومقارنة أدائه إلى النماذج والأداء الأسود الميزة لتقييم الأهمية النسبية للميزات القابلة للتفسير البشرية في التمييز بين الأخبار المزيفة من الأخبار الأصلية وبعد
As the world continues to fight the COVID-19 pandemic, it is simultaneously fighting an infodemic' -- a flood of disinformation and spread of conspiracy theories leading to health threats and the division of society. To combat this infodemic, there is an urgent need for benchmark datasets that can help researchers develop and evaluate models geared towards automatic detection of disinformation. While there are increasing efforts to create adequate, open-source benchmark datasets for English, comparable resources are virtually unavailable for German, leaving research for the German language lagging significantly behind. In this paper, we introduce the new benchmark dataset FANG-COVID consisting of 28,056 real and 13,186 fake German news articles related to the COVID-19 pandemic as well as data on their propagation on Twitter. Furthermore, we propose an explainable textual- and social context-based model for fake news detection, compare its performance to black-box'' models and perform feature ablation to assess the relative importance of human-interpretable features in distinguishing fake news from authentic news.
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