A Human in the Loop Approach to Capture Bias and Support Media Scientists in News Video Analysis

Abstract

Bias is inevitable and inherent in any form of communication. News often appear biased to citizens with different political orientations, and understood differently by news media scholars and the broader public. In this paper we advocate the need for accurate methods for bias identification in video news item, to enable rich analytics capabilities in order to assist humanities media scholars and social political scientists. We propose to analyze biases that are typical in video news (including framing, gender and racial biases) by means of a human-in-the-loop approach that combines text and image analysis with human computation techniques.