In this talk, we introduce our research on a major problem of journalism, i.e., the media bias problem. Bias of the news media is an inherent flaw of the news production process, spanning news gathering, writing, and editing stages. At every single stage, news is probably never free from the producer’s subjective valuation and external forces such as owners and advertisers. While the problem has been extensively studied in the area of mass communication and journalism, effective solutions are barely developed. Our research investigates the media bias problem from a computational perspective and proposes a practical approach ‘media bias mitigation’. Admitting the prevalence of bias, the approach attempts to reduce the effects of bias rather than to prove or correct it. Our work aims to provide readers with tools for active interaction with which they easily discover and compare diversity of existing biased views. We provide an overview of the works dealing with three important news article domains: NewsCube system supporting aspect-level browsing of straight news articles; Commenter’s sentiment pattern-based analysis of political news articles; and Disputant relation-based method for news articles of contentious news issues.