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

Myrthe Reuver, Antske Fokkens, Suzan Verberne


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.
Anthology ID:
2021.hackashop-1.7
Volume:
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation
Month:
April
Year:
2021
Address:
Online
Venues:
EACL | Hackashop
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
45–55
Language:
URL:
https://aclanthology.org/2021.hackashop-1.7
DOI:
Bibkey:
Cite (ACL):
Myrthe Reuver, Antske Fokkens, and Suzan Verberne. 2021. No NLP Task Should be an Island: Multi-disciplinarity for Diversity in News Recommender Systems. In Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation, pages 45–55, Online. Association for Computational Linguistics.
Cite (Informal):
No NLP Task Should be an Island: Multi-disciplinarity for Diversity in News Recommender Systems (Reuver et al., Hackashop 2021)
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PDF:
https://aclanthology.org/2021.hackashop-1.7.pdf