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
EditorsLora Aroyo, Anca Dumitrache, Praveen Paritosh, Alex Quinn, Chris Welty, Alessandro Checco, Gianluca Demartini, Ujwal Gadiraju, Cristina Sarasua
PublisherCEUR
Pages88-92
Number of pages5
Volume2276
Publication statusPublished - 2018
Event1st Workshop on Subjectivity, Ambiguity and Disagreement in Crowdsourcing, and 1st Workshop on Disentangling the Relation Between Crowdsourcing and Bias Management - University of Zurich, Zurich, Switzerland
Duration: 5 Jul 20185 Jul 2018
https://sites.google.com/view/crowdbias

Publication series

NameCEUR Workshop Proceedings
Volume2276
ISSN (Electronic)1613-0073

Conference

Conference1st Workshop on Subjectivity, Ambiguity and Disagreement in Crowdsourcing, and 1st Workshop on Disentangling the Relation Between Crowdsourcing and Bias Management
Abbreviated titleSAD2018 CrowdBias2018
CountrySwitzerland
CityZurich
Period5/07/185/07/18
Internet address

    Research areas

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

ID: 51570495