Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in
  News Reporting
Kaijian Zou Xinliang Frederick Zhang Winston Wu Nick Beauchamp Lu Wang
News media is expected to uphold unbiased reporting. Yet they may still
affect public opinion by selectively including or omitting events that support
or contradict their ideological positions. Prior work in NLP has only studied
media bias via linguistic style and word usage. In this paper, we study to
which degree media balances news reporting and affects consumers through event
inclusion or omission. We first introduce the task of detecting both partisan
and counter-partisan events: events that support or oppose the author's
political ideology. To conduct our study, we annotate a high-quality dataset,
PAC, containing 8,511 (counter-)partisan event annotations in 304 news articles
from ideologically diverse media outlets. We benchmark PAC to highlight the
challenges of this task. Our findings highlight both the ways in which the news
subtly shapes opinion and the need for large language models that better
understand events within a broader context. Our dataset can be found at
https://github.com/launchnlp/Partisan-Event-Dataset.