Context in Informational Bias Detection

TL;DR

This paper investigates how different types of context, such as neighboring sentences and related articles, influence the detection of informational bias in news articles, showing that context improves classification accuracy.

Contribution

It introduces the exploration of multiple context types for informational bias detection and demonstrates their impact on model performance and error analysis.

Findings

Event context improves classification accuracy.

Context-inclusive models perform better on longer sentences.

Models perform well on politically centrist articles.

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.