TY - GEN
T1 - Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content
AU - Reuver, Myrthe
AU - Mattis, Nick
AU - Sax, Marijn
AU - Verberne, Suzan
AU - Tintarev, Nava
AU - Helberger, Natali
AU - Moeller, Judith
AU - Vrijenhoek, Sanne
AU - Fokkens, Antske
AU - van Atteveldt, Wouter
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
U2 - 10.18653/v1/2021.nlp4posimpact-1.6
DO - 10.18653/v1/2021.nlp4posimpact-1.6
M3 - Conference contribution
SP - 47
EP - 59
BT - Proceedings of the 1st Workshop on NLP for Positive Impact
PB - Association for Computational Linguistics, ACL Anthology
ER -