Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/265109 
Year of Publication: 
2022
Citation: 
[Editor:] Schaefer, Robin [Editor:] Bai, Xiaoyu [Editor:] Stede, Manfred [Editor:] Zesch, Torsten [Title:] Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022) [Publisher:] KONVENS 2022 Organizers [Place:] Potsdam [Year:] 2022 [Pages:] 29-40
Publisher: 
KONVENS 2022 Organizers, Potsdam
Abstract: 
We introduce a new bi-dimensional classification scheme for political bias. In particular, we collaborate with political scientists and identify two important aspects: cultural and socioeconomic positions. Using a dataset of tweets by German politicians, we show that the new scheme draws more distinctive boundaries that are easier to model for machine learning classifiers (F1 scores: 0.92 and 0.86), compared to one-dimensional approaches. We investigate the validity by applying the new classifiers to the whole dataset, including previously unseen data from other parties. Additional experiments highlight the importance of dataset size and balance, as well as the superior performance of transformer language models as opposed to older methods. Finally, an extensive error analysis confirms our hypothesis that lexical overlap, in combination with high attention values, is a reliable empirical predictor of misclassification for political bias.
Creative Commons License: 
cc-by Logo
Document Type: 
Book Part
Document Version: 
Published Version

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