mediabiasgroup/da-roberta-pt

This is a model pre-trained on weak labels for media-bias detection.

---

Citation

Code repository: https://github.com/Media-Bias-Group/Neural-Media-Bias-Detection-Using-Distant-Supervision-With-BABE

Paper: The paper is avalable at: https://aclanthology.org/2021.findings-emnlp.101

If you use this model, please cite the following paper(s):

@inproceedings{spinde-etal-2021-neural-media,
    title = "Neural Media Bias Detection Using Distant Supervision With {BABE} - Bias Annotations By Experts",
    author = "Spinde, Timo  and
      Plank, Manuel  and
      Krieger, Jan-David  and
      Ruas, Terry  and
      Gipp, Bela  and
      Aizawa, Akiko",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.101",
    doi = "10.18653/v1/2021.findings-emnlp.101",
    pages = "1166--1177",
    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.",
}