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Register a new userFine-grained Classification of Political Bias in German News: A Data Set and Initial Experiments
التصنيف الجميل المحبوس من التحيز السياسي في الأخبار الألمانية: مجموعة بيانات والتجارب الأولية
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نقدم مجموعة بيانات تتكون من مقالات إخبارية ألمانية تسمى التحيز السياسي على مقياس من خمس نقاط في طريقة نصف إشراف.في حين أن العمل المبكر على الكشف عن الأخبار Hyperpartisan يستخدم التصنيف الثنائي (أي Hyperpartisan أو لا) وبيانات اللغة الإنجليزية، فإننا نقول للحصول على تصنيف أكثر غرامة، تغطي الطيف السياسي الكامل (أي بعيدا، اليسار، اليسار، المركز، اليمين، بعيدا- رايت) ولمنس البحث إلى البيانات الألمانية.فهم التحيز السياسي يساعد بدقة في اكتشاف خطاب الكراهية وإساءة الاستخدام عبر الإنترنت.نقوم بتجربة أساليب تصنيف مختلفة للكشف عن التحيز السياسي.تؤكد أدائها المنخفض نسبيا (ماكرو-F1 من 43 من أفضل إعداد لدينا، مقارنة ب Macro-F1 من 79 بمهمة التصنيف الثنائية) إلى الحاجة إلى بيانات أكثر (متوازنة) المشروح بطريقة محترمة بشكل جيد.
We present a data set consisting of German news articles labeled for political bias on a five-point scale in a semi-supervised way. While earlier work on hyperpartisan news detection uses binary classification (i.e., hyperpartisan or not) and English data, we argue for a more fine-grained classification, covering the full political spectrum (i.e., far-left, left, centre, right, far-right) and for extending research to German data. Understanding political bias helps in accurately detecting hate speech and online abuse. We experiment with different classification methods for political bias detection. Their comparatively low performance (a macro-F1 of 43 for our best setup, compared to a macro-F1 of 79 for the binary classification task) underlines the need for more (balanced) data annotated in a fine-grained way.
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