@inproceedings{aksenov-etal-2021-fine,
title = "Fine-grained Classification of Political Bias in {G}erman News: A Data Set and Initial Experiments",
author = "Aksenov, Dmitrii and
Bourgonje, Peter and
Zaczynska, Karolina and
Ostendorff, Malte and
Moreno-Schneider, Julian and
Rehm, Georg",
editor = "Mostafazadeh Davani, Aida and
Kiela, Douwe and
Lambert, Mathias and
Vidgen, Bertie and
Prabhakaran, Vinodkumar and
Waseem, Zeerak",
booktitle = "Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.woah-1.13",
doi = "10.18653/v1/2021.woah-1.13",
pages = "121--131",
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.",
}
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%0 Conference Proceedings
%T Fine-grained Classification of Political Bias in German News: A Data Set and Initial Experiments
%A Aksenov, Dmitrii
%A Bourgonje, Peter
%A Zaczynska, Karolina
%A Ostendorff, Malte
%A Moreno-Schneider, Julian
%A Rehm, Georg
%Y Mostafazadeh Davani, Aida
%Y Kiela, Douwe
%Y Lambert, Mathias
%Y Vidgen, Bertie
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F aksenov-etal-2021-fine
%X 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.
%R 10.18653/v1/2021.woah-1.13
%U https://aclanthology.org/2021.woah-1.13
%U https://doi.org/10.18653/v1/2021.woah-1.13
%P 121-131
Markdown (Informal)
[Fine-grained Classification of Political Bias in German News: A Data Set and Initial Experiments](https://aclanthology.org/2021.woah-1.13) (Aksenov et al., WOAH 2021)
ACL