Illegal Aliens or Undocumented Immigrants? Towards the Automated Identification of Bias by Word Choice and Labeling

Abstract

Media bias, i.e., slanted news coverage, can strongly impact the public perception of topics reported in the news. While the analysis of media bias has recently gained attention in computer science, the automated methods and results tend to be simple when compared to approaches and results in the social sciences, where researchers have studied media bias for decades. We propose Newsalyze, a work-in-progress prototype that imitates a manual analysis concept for media bias established in the social sciences. Newsalyze aims to find instances of bias by word choice and labeling in a set of news articles reporting on the same event. Bias by word choice and labeling (WCL) occurs when journalists use different phrases to refer to the same semantic concept, e.g., actors or actions. This way, instances of bias by WCL can induce strongly divergent emotional responses from readers, such as the terms “illegal aliens” vs. “undocumented immigrants.” We describe two critical tasks of the analysis workflow, finding and mapping such phrases, and estimating their effects on readers. For both tasks, we also present first results, which indicate the effectiveness of exploiting methods and models from the social sciences in an automated approach.