Publication
Title
News diversity and recommendation systems : setting the interdisciplinary scene
Author
Abstract
Concerns about selective exposure and filter bubbles in the digital news environment trigger questions regarding how news recommender systems can become more citizen-oriented and facilitate – rather than limit – normative aims of journalism. Accordingly, this chapter presents building blocks for the construction of such a news algorithm as they are being developed by the Ghent University interdisciplinary research project #NewsDNA, of which the primary aim is to actually build, evaluate and test a diversity-enhancing news recommender. As such, the deployment of artificial intelligence could support the media in providing people with information and stimulating public debate, rather than undermine their role in that respect. To do so, it combines insights from computer sciences (news recommender systems), law (right to receive information), communication sciences (conceptualisations of news diversity), and computational linguistics (automated content extraction from text). To gather feedback from scholars of different backgrounds, this research has been presented and discussed during the 2019 IFIP summer school workshop on ‘co-designing a personalised news diversity algorithmic model based on news consumers’ agency and fine-grained content modelling’. This contribution also reflects the results of that dialogue.
Language
English
Source (book)
Privacy and identity management : data for better living: AI and privacy / Friedewald, M. [edit.]; et al.
Source (series)
IFIP advances in information and communication technology
Publication
Cham : Springer , 2020
ISBN
978-3-030-42503-6
DOI
10.1007/978-3-030-42504-3_7
Volume/pages
p. 90-105
Full text (Publisher's DOI)
UAntwerpen
Publication type
Subject
External links
Record
Identifier
Creation 22.02.2024
Last edited 22.02.2024
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