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Fine-grained Classification of Political Bias in German News: A Data Set and Initial Experiments

Dmitrii Aksenov; Peter Bourgonje; Karolina Zaczynska; Malte Ostendorff; Julian Moreno Schneider; Georg Rehm
In: Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021). Workshop on Online Abuse and Harms (WOAH-2021), located at ACL-IJCNLP, August 1-6, Online, Thailand, Pages 121-131, Association for Computational Linguistics, 2021.


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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