  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.
