No NLP Task Should be an Island: Multi-disciplinarity for Diversity in News Recommender Systems

M.E. Reuver, Antske Fokkens, Suzan Verberne

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Natural Language Processing (NLP) is defined by specific, separate tasks, with each their own literature, benchmark datasets, and definitions. In this position paper, we argue that for a complex problem such as the threat to democracy by non-diverse news recommender systems, it is important to take into account a higher-order, normative goal and its implications. Experts in ethics, political science and media studies have suggested that news recommendation systems could be used to support a deliberative democracy. We reflect on the role of NLP in recommendation systems with this specific goal in mind and show that this theory of democracy helps to identify which NLP tasks and techniques can support this goal, and what work still needs to be done. This leads to recommendations for NLP researchers working on this specific problem as well as researchers working on other complex multidisciplinary problems.
Original languageEnglish
Title of host publicationProceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation (co-located at EACL 2021, online)
EditorsHannu Toivonen, Michele Boggia
PublisherAssociation of Computational Linguistics
Pages45–55
Number of pages11
Publication statusPublished - Apr 2021
EventEACL Hackashop on News Media Content Analysis and Automated Report Generation (co-located at EACL 2021, online) -
Duration: 19 Apr 202119 Apr 2021
http://embeddia.eu/hackashop2021/

Conference

ConferenceEACL Hackashop on News Media Content Analysis and Automated Report Generation (co-located at EACL 2021, online)
Period19/04/2119/04/21
Internet address

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