Context in Informational Bias Detection

Proceedings of the 28th International Conference on Computational Linguistics, pages 6315–6326 Barcelona, Spain (Online), December 8-13, 2020 6315 Context in Informational Bias Detection Esther van den Berg Leibniz ScienceCampus Heidelberg/Mannheim, Germany berg@ids-mannheim.de Katja Markert Institute of Computational Linguistics Heidelberg University, Germany markert@cl.uni-heidelberg.de Abstract Informational bias is bias conveyed through sentences or clauses that provide tangential, spec- ulative 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 per- formance 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. 1 Introduction Informational bias is conveyed through sentences or clauses that provide tangential, speculative or back- ground information that can sway readers’ opinions towards entities (Fan et al., 2019). A natural place to look for informational bias is in news texts, where journalists use background information to place newsworthy events in a broader context. Examples of informational bias include quotations of opinions from third parties about the target entity, allusions to what may have motivated the target entity to act as they did, and mentions of previous statements and actions of the same entity. What separates informational bias from other kinds of bias is that it can be expressed in a completely factual and neutral way. While some instances of bias are recognisable outside of their context (e.g. quotations from third parties that contain opinions), others are mere statements of facts that do not raise suspicions of bias outside of their context. Consider example instance 1.3 in Table 1. This sentence contains no subjective language. Seen on its own, it is simply stating a fact. However, human annotators judged that it is a case informational bias (Fan et al., 2019). This is because this particular fact reflects positively on the target entity Mike Huckabee in the context of an announcement that he is running for president. Note that one can also imagine contexts where the implication is negative. An exam- ple would be a discussion of a disconnect between older Republican candidates and a new generation of more progressive voters. The fact that instances of informational bias can be superficially neutral and are context-dependent makes informational bias detection an exceptionally challenging task, which furthermore has a short research history and few available relevant resources. While previous work has performed informational bias detection on a token level and a sentence level, we are the first to involve context beyond the sentence. We integrate four kinds of context: neighboring sentences (direct textual context), the full …

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