A Human in the Loop Approach to Capture Bias and Support Media Scientists in News Video Analysis
Panagiotis Mavridis (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Markus de Jong (Vrije Universiteit Amsterdam)
Lora Aroyo (Universiteit Leiden)
Alessandro Bozzon (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Jesse de Vos (Nederlands Instituut voor Beeld en Geluid)
Johan Oomen (Nederlands Instituut voor Beeld en Geluid)
Antoaneta Dimitrova (Universiteit Leiden)
Alec Badenoch (Universiteit Utrecht)
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Abstract
Bias is inevitable and inherent in any form of communication. News often appear biased to citizens with dierent political orientations, and understood dierently by news media scholars and the broader public. In this paper we advocate the need for accurate methods for bias identication 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.