@inproceedings{van-den-berg-markert-2020-context,
title = "Context in Informational Bias Detection",
author = "van den Berg, Esther and
Markert, Katja",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.556",
doi = "10.18653/v1/2020.coling-main.556",
pages = "6315--6326",
abstract = "Informational bias is bias conveyed through sentences or clauses that provide tangential, speculative or background information that can sway readers{'} opinions towards entities. By nature, informational bias is context-dependent, but previous work on informational bias detection has not explored the role of context beyond the sentence. In this paper, we explore four kinds of context for informational bias in English news articles: neighboring sentences, the full article, articles on the same event from other news publishers, and articles from the same domain (but potentially different events). We find that integrating event context improves classification performance over a very strong baseline. In addition, we perform the first error analysis of models on this task. We find that the best-performing context-inclusive model outperforms the baseline on longer sentences, and sentences from politically centrist articles.",
}
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%0 Conference Proceedings
%T Context in Informational Bias Detection
%A van den Berg, Esther
%A Markert, Katja
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F van-den-berg-markert-2020-context
%X Informational bias is bias conveyed through sentences or clauses that provide tangential, speculative or background information that can sway readers’ opinions towards entities. By nature, informational bias is context-dependent, but previous work on informational bias detection has not explored the role of context beyond the sentence. In this paper, we explore four kinds of context for informational bias in English news articles: neighboring sentences, the full article, articles on the same event from other news publishers, and articles from the same domain (but potentially different events). We find that integrating event context improves classification performance over a very strong baseline. In addition, we perform the first error analysis of models on this task. We find that the best-performing context-inclusive model outperforms the baseline on longer sentences, and sentences from politically centrist articles.
%R 10.18653/v1/2020.coling-main.556
%U https://aclanthology.org/2020.coling-main.556
%U https://doi.org/10.18653/v1/2020.coling-main.556
%P 6315-6326
Markdown (Informal)
[Context in Informational Bias Detection](https://aclanthology.org/2020.coling-main.556) (van den Berg & Markert, COLING 2020)
ACL
- Esther van den Berg and Katja Markert. 2020. Context in Informational Bias Detection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6315–6326, Barcelona, Spain (Online). International Committee on Computational Linguistics.