A computational approach to media bias mitigation

Abstract

The bias in the news media is an inherent flaw of the news production process. The resulting bias often causes a sharp increase in political polarization and in the cost of conflict on social issues such as the Iraq war. It is very difficult, if not impossible, for readers to have penetrating views on realities against such bias. The problem has been extensively studied considering its serious and chronic impact. News producers put much effort to avoid the creation of bias at the production stage, e.g., journalism ethics and standards and adversarial formats of reporting such as point-counterpoint roundtable discussions. Media experts and researchers make efforts such as frame analysis to deal with the created biases of the produced contents. However, media bias is still widespread. This thesis investigates the media bias problem from a computational perspective and develops a practical solution. We present the approach media bias mitigation, reducing the effects of bias by making readers themselves to actively overcome biased views. The thesis aims to establish a framework providing readers with tools for active interaction with which they easily discover and compare diversity of existing biased views. For this, we develop the bias mitigation approach for three important news article domains: straight news articles, news articles on contentious issues, and political news articles. The first part of the thesis presents aspect-level news browsing as a solution to mitigate bias in straight news articles. Aspect-level news browsing provides readers with a classified view of news articles on the same event with different viewpoints. It effectively helps readers understand the event from a plural of viewpoints and formulate their own, more balanced viewpoints free from specific biased views. Realizing aspect-level browsing raises important challenges, mainly due to the lack of semantic knowledge with which to abstract and classify the intended salient aspects of ar...