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

Conference paper (2018)

Authors

P. Mavridis Web Information Systems - EEMCS
M. de Jong Vrije Universiteit Amsterdam
Lora Aroyo Universiteit Leiden
A. Bozzon Web Information Systems - EEMCS
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
Research Group
Web Information Systems (EEMCS) (TU Delft)
Copyright: © 2018 P. Mavridis, M. de Jong, Lora Aroyo, A. Bozzon, Jesse de Vos, Johan Oomen, Antoaneta Dimitrova, Alec Badenoch
Machine learning Crowdsourcing Human computation Human in the loop Bias detection Bias in news video files
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Published Date

2018

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

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.

Files

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