What's in the News? Towards Identification of Bias by Commission, Omission, and Source Selection (COSS)

TL;DR

This paper introduces a unified methodology for automatically detecting bias in news articles, focusing on commission, omission, and source selection, to improve understanding of information neutrality.

Contribution

It presents a joint approach to identify multiple bias types simultaneously, advancing beyond previous methods that addressed biases separately.

Findings

Proposes a pipeline for bias detection in news articles.

Includes a visualization example leveraging extracted bias features.

Addresses the challenge of assessing neutrality in news reporting.

Abstract

In a world overwhelmed with news, determining which information comes from reliable sources or how neutral is the reported information in the news articles poses a challenge to news readers. In this paper, we propose a methodology for automatically identifying bias by commission, omission, and source selection (COSS) as a joint three-fold objective, as opposed to the previous work separately addressing these types of bias. In a pipeline concept, we describe the goals and tasks of its steps toward bias identification and provide an example of a visualization that leverages the extracted features and patterns of text reuse.