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Register a new userNeural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts
كشف وسائل الإعلام العصبية باستخدام إشراف بعيد مع فاتنة - التعليقات التوضيحية من قبل الخبراء
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تغطية إعلامية لها تأثير كبير على التصور العام للأحداث. ومع ذلك، فإن وسائل الإعلام هي في كثير من الأحيان منحازة. طريقة واحدة إلى المقالات الإخبارية Bias هي تغيير اختيار الكلمة. يعد التعريف التلقائي للتحيز من خلال اختيار Word صعبة، ويرجع ذلك أساسا إلى عدم وجود مجموعة بيانات قياسية ذهبية وتبعية عالية السياق. تقدم هذه الورقة فاتنة، وهي مجموعة بيانات قوية ومتنوعة تم إنشاؤها بواسطة خبراء مدربين، بحثا عن أبحاث وسائل الإعلام. نحن نحلل أيضا سبب أهمية وضع العلامات الخبيرة ضمن هذا المجال. توفر مجموعة البيانات الخاصة بنا جودة توضيحية أفضل واتفاقية أعلى معلقا من العمل الحالي. وهي تتألف من 3700 جمل متوازنة بين الموضوعات والمنافذ، تحتوي على تسميات وسائل الإعلام على مستوى الكلمة ومستوى الجملة. بناء على بياناتنا، نقدم أيضا وسيلة للكشف عن الجمل التي تحفز التحيز في مقالات إخبارية تلقائيا. يتم تدريب أفضل ما لدينا من النموذج المستند في بيرت المدرب مسبقا على كوربوس أكبر يتكون من ملصقات بعيدة. قم بضبط وتقييم النموذج على مجموعة البيانات الخاضعة للإشراف المقترحة، ونحن نحقق درجة ماكرو F1 من 0.804، مما يتفوق على الأساليب الحالية.
Media coverage has a substantial effect on the public perception of events. Nevertheless, media outlets are often biased. One way to bias news articles is by altering the word choice. The automatic identification of bias by word choice is challenging, primarily due to the lack of a gold standard data set and high context dependencies. This paper presents BABE, a robust and diverse data set created by trained experts, for media bias research. We also analyze why expert labeling is essential within this domain. Our data set offers better annotation quality and higher inter-annotator agreement than existing work. It consists of 3,700 sentences balanced among topics and outlets, containing media bias labels on the word and sentence level. Based on our data, we also introduce a way to detect bias-inducing sentences in news articles automatically. Our best performing BERT-based model is pre-trained on a larger corpus consisting of distant labels. Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0.804, outperforming existing methods.
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