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.
Original languageEnglish
Title of host publicationProceedings of the 1st Workshop on Subjectivity, Ambiguity and Disagreement in Crowdsourcing, and Short Paper Proceedings of the 1st Workshop on Disentangling the Relation Between Crowdsourcing and Bias Management
Number of pages5
Publication statusPublished - 2018

    Research areas

  • Bias detection, bias in news video files, machine learning, crowdsourcing, human computation, human in the loop

ID: 51570495