Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content

Myrthe Reuver, Nick Mattis, Marijn Sax, Suzan Verberne, Nava Tintarev, Natali Helberger, Judith Moeller, Sanne Vrijenhoek, Antske Fokkens, Wouter van Atteveldt

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

Abstract

In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation. Recommending news from diverse viewpoints is important to prevent potential filter bubble effects in news consumption, and stimulate a healthy democratic debate.To account for the complexity that is inherent to humans as citizens in a democracy, we anticipate (among others) individual-level differences in acceptance of diversity. We connect this idea to techniques in Natural Language Processing, where distributional language models would allow us to place different users and news articles in a multidimensional space based on semantic content, where diversity is operationalized as distance and variance. In this way, we can model individual “latitudes of diversity” for different users, and thus personalize viewpoint diversity in support of a healthy public debate. In addition, we identify technical, ethical and conceptual issues related to our presented ideas. Our investigation describes how NLP can play a central role in diversifying news recommendations.
Original languageEnglish
Title of host publicationAre we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content
PublisherAssociation for Computational Linguistics, ACL Anthology
Pages47-59
Number of pages12
DOIs
Publication statusPublished - 1 Jul 2021

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