Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts

Timo Spinde, Manuel Plank, Jan-David Krieger, Terry Ruas, Bela Gipp, Akiko Aizawa


Abstract
Media coverage has a substantial effect on the public perception of events. Nevertheless, media outlets are often biased. One way to bias news articles is by altering the word choice. The automatic identification of bias by word choice is challenging, primarily due to the lack of a gold standard data set and high context dependencies. This paper presents BABE, a robust and diverse data set created by trained experts, for media bias research. We also analyze why expert labeling is essential within this domain. Our data set offers better annotation quality and higher inter-annotator agreement than existing work. It consists of 3,700 sentences balanced among topics and outlets, containing media bias labels on the word and sentence level. Based on our data, we also introduce a way to detect bias-inducing sentences in news articles automatically. Our best performing BERT-based model is pre-trained on a larger corpus consisting of distant labels. Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0.804, outperforming existing methods.
Anthology ID:
2021.findings-emnlp.101
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1166–1177
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.101
DOI:
10.18653/v1/2021.findings-emnlp.101
Bibkey:
Cite (ACL):
Timo Spinde, Manuel Plank, Jan-David Krieger, Terry Ruas, Bela Gipp, and Akiko Aizawa. 2021. Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1166–1177, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts (Spinde et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.101.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.101.mp4
Code
 media-bias-analysis-group/neural-media-bias-detection-using-distant-supervision-with-babe