What's in the News? Towards Identification of Bias by Commission, Omission, and Source Selection (COSS)
This addresses the challenge for news readers in assessing reliability and neutrality in news sources, though it appears incremental by combining previously separate bias types.
The paper tackles the problem of identifying bias in news articles by proposing a methodology for automatically detecting bias by commission, omission, and source selection (COSS) as a joint three-fold objective, with results including an example visualization leveraging extracted features and patterns of text reuse.
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.