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Register a new userMasking and Transformer-based Models for Hyperpartisanship Detection in News
نماذج اخفاء ومحول للكشف عن ارتفاع ضغط الدم في الأخبار
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تظهر أخبار Hyperpartisan التلاعب الشديد بالحقيقة بناء على اتجاه أيديولوجي أساسي ومحد للغاية. نظرا لآثارها الضارة في تعزيز تحيز الفرد والسلوك الخلفي للأشخاص، أصبح الكشف عن الأخبار Hyperpartisan مهمة مهمة بالنسبة لغوياء الحساسين. في هذه الورقة، نقوم بتقييم طريقتين مختلفتين للكشف عن أخبار فرط الاسباريسان. أولا، تقنية إخفاء نصية تسمح لنا بمقارنة الميزات المتعلقة بالموضوعات ذات الصلة بالموضوع في منظور مختلف عن العمل السابق. ثانيا، نماذج المحولات التي تعتمد على المحولات، XLM-roberta، و m-bert، المعروف بقدرتها على التقاط أنماط دلالية ونقص في نفس التمثيل. تؤكد نتائجنا البحث السابق في هذه المهمة في هذه الميزات المتعلقة بالموضوعات التي تسفر عن نتائج أفضل من تلك القائمة على النمط، على الرغم من أنها تسليط الضوء أيضا على أهمية استخدام N-WIND أعلى N-Grams. علاوة على ذلك، فإنها تظهر أن النماذج القائمة على المحولات هي أكثر فعالية من الأساليب التقليدية، ولكن هذا بتكلفة تعقيد حسابي أكبر وعدم الشفافية. استنادا إلى تجاربنا، نستنتج أن بداية الأخبار تظهر المعلومات ذات الصلة للمحولات في التمييز بفعالية بين الاتجاهات اليسارية والسائدة واليمين.
Hyperpartisan news show an extreme manipulation of reality based on an underlying and extreme ideological orientation. Because of its harmful effects at reinforcing one's bias and the posterior behavior of people, hyperpartisan news detection has become an important task for computational linguists. In this paper, we evaluate two different approaches to detect hyperpartisan news. First, a text masking technique that allows us to compare style vs. topic-related features in a different perspective from previous work. Second, the transformer-based models BERT, XLM-RoBERTa, and M-BERT, known for their ability to capture semantic and syntactic patterns in the same representation. Our results corroborate previous research on this task in that topic-related features yield better results than style-based ones, although they also highlight the relevance of using higher-length n-grams. Furthermore, they show that transformer-based models are more effective than traditional methods, but this at the cost of greater computational complexity and lack of transparency. Based on our experiments, we conclude that the beginning of the news show relevant information for the transformers at distinguishing effectively between left-wing, mainstream, and right-wing orientations.
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