Abstract

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.

Highlights

  • The social web and social media networks have received an ever-increasing amount of attention since their emergence 15-20 years ago

  • This task’s test/evaluation data comprised English news articles and used labels obtained by Vincent and Mestre (2018), but their five-point scale was binarised so the challenge was to label articles as being either hyperpartisan or not hyperpartisan

  • We first apply our models to the 2019 Hyperpartisan News Detection task

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Summary

Introduction

The social web and social media networks have received an ever-increasing amount of attention since their emergence 15-20 years ago Their popularity among billions of users has had a significant effect on the way people consume information in general, and news in particular (Newman et al, 2016). The prediction of political bias was recently examined by the 2019 Hyperpartisan News Detection task (Kiesel et al, 2019) with 42 teams submitting valid runs, resulting in over 30 publications This task’s test/evaluation data comprised English news articles and used labels obtained by Vincent and Mestre (2018), but their five-point scale was binarised so the challenge was to label articles as being either hyperpartisan or not hyperpartisan

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