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Register a new userLeveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models
الاستفادة من المجتمع والسياق المؤلف لشرح أداء وتحيز نماذج الكشف عن الخداع القائمة على النص
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يمكن اكتشاف الوظائف الإخبارية الخادعة المشتركة في المجتمعات عبر الإنترنت مع نماذج NLP، وقد ركزت البحوث الحديثة الكثير على تطوير هذه النماذج.في هذا العمل، نستخدم خصائص المجتمعات والمؤلفين عبر الإنترنت --- سياق كيفية نشر المحتوى - - لشرح أداء نموذج كشف الخداع الشبكي العصبي وتحديد السكان الفرعيين الذين يتأثرون بشكل غير متناسب بدقة نموذجيةأو الفشل.نحن ندرس من يقوم بنشر المحتوى، وحيث يتم نشر المحتوى إليه.نجد أنه في حين أن خصائص المؤلف هي أفضل من المتنبئين من المحتوى الخادع من الخصائص المجتمعية، فإن كلا الخصائص مرتبطة بقوة بأداء نموذجي.قد تفشل مقاييس الأداء التقليدية مثل درجة F1 في التقاط أداء نموذجي ضعيف على السكان الفرعيين المعزولين مثل المؤلفين المحددين، وعلى هذا النحو، فإن التقييم الأكثر دقة لنماذج الكشف عن الخداع أمر بالغ الأهمية.
Deceptive news posts shared in online communities can be detected with NLP models, and much recent research has focused on the development of such models. In this work, we use characteristics of online communities and authors --- the context of how and where content is posted --- to explain the performance of a neural network deception detection model and identify sub-populations who are disproportionately affected by model accuracy or failure. We examine who is posting the content, and where the content is posted to. We find that while author characteristics are better predictors of deceptive content than community characteristics, both characteristics are strongly correlated with model performance. Traditional performance metrics such as F1 score may fail to capture poor model performance on isolated sub-populations such as specific authors, and as such, more nuanced evaluation of deception detection models is critical.
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