@inproceedings{reuver-etal-2021-human,
title = "Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content",
author = "Reuver, Myrthe and
Mattis, Nicolas and
Sax, Marijn and
Verberne, Suzan and
Tintarev, Nava and
Helberger, Natali and
Moeller, Judith and
Vrijenhoek, Sanne and
Fokkens, Antske and
van Atteveldt, Wouter",
editor = "Field, Anjalie and
Prabhumoye, Shrimai and
Sap, Maarten and
Jin, Zhijing and
Zhao, Jieyu and
Brockett, Chris",
booktitle = "Proceedings of the 1st Workshop on NLP for Positive Impact",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4posimpact-1.6",
doi = "10.18653/v1/2021.nlp4posimpact-1.6",
pages = "47--59",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content
%A Reuver, Myrthe
%A Mattis, Nicolas
%A Sax, Marijn
%A Verberne, Suzan
%A Tintarev, Nava
%A Helberger, Natali
%A Moeller, Judith
%A Vrijenhoek, Sanne
%A Fokkens, Antske
%A van Atteveldt, Wouter
%Y Field, Anjalie
%Y Prabhumoye, Shrimai
%Y Sap, Maarten
%Y Jin, Zhijing
%Y Zhao, Jieyu
%Y Brockett, Chris
%S Proceedings of the 1st Workshop on NLP for Positive Impact
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F reuver-etal-2021-human
%X 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.
%R 10.18653/v1/2021.nlp4posimpact-1.6
%U https://aclanthology.org/2021.nlp4posimpact-1.6
%U https://doi.org/10.18653/v1/2021.nlp4posimpact-1.6
%P 47-59
Markdown (Informal)
[Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content](https://aclanthology.org/2021.nlp4posimpact-1.6) (Reuver et al., NLP4PI 2021)
ACL
- Myrthe Reuver, Nicolas Mattis, Marijn Sax, Suzan Verberne, Nava Tintarev, Natali Helberger, Judith Moeller, Sanne Vrijenhoek, Antske Fokkens, and Wouter van Atteveldt. 2021. Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content. In Proceedings of the 1st Workshop on NLP for Positive Impact, pages 47–59, Online. Association for Computational Linguistics.