@inproceedings{dufraisse-etal-2022-dont,
title = "Don{'}t Burst Blindly: For a Better Use of Natural Language Processing to Fight Opinion Bubbles in News Recommendations",
author = "Dufraisse, Evan and
Treuillier, C{\'e}lina and
Brun, Armelle and
Tourille, Julien and
Castagnos, Sylvain and
Popescu, Adrian",
editor = "Afli, Haithem and
Alam, Mehwish and
Bouamor, Houda and
Casagran, Cristina Blasi and
Boland, Colleen and
Ghannay, Sahar",
booktitle = "Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.politicalnlp-1.11",
pages = "79--85",
abstract = "Online news consumption plays an important role in shaping the political opinions of citizens. The news is often served by recommendation algorithms, which adapt content to users{'} preferences. Such algorithms can lead to political polarization as the societal effects of the recommended content and recommendation design are disregarded. We posit that biases appear, at least in part, due to a weak entanglement between natural language processing and recommender systems, both processes yet at work in the diffusion and personalization of online information. We assume that both diversity and acceptability of recommended content would benefit from such a synergy. We discuss the limitations of current approaches as well as promising leads of opinion-mining integration for the political news recommendation process.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dufraisse-etal-2022-dont">
<titleInfo>
<title>Don’t Burst Blindly: For a Better Use of Natural Language Processing to Fight Opinion Bubbles in News Recommendations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Evan</namePart>
<namePart type="family">Dufraisse</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Célina</namePart>
<namePart type="family">Treuillier</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Armelle</namePart>
<namePart type="family">Brun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Julien</namePart>
<namePart type="family">Tourille</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sylvain</namePart>
<namePart type="family">Castagnos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Adrian</namePart>
<namePart type="family">Popescu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences</title>
</titleInfo>
<name type="personal">
<namePart type="given">Haithem</namePart>
<namePart type="family">Afli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mehwish</namePart>
<namePart type="family">Alam</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Houda</namePart>
<namePart type="family">Bouamor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Cristina</namePart>
<namePart type="given">Blasi</namePart>
<namePart type="family">Casagran</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Colleen</namePart>
<namePart type="family">Boland</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sahar</namePart>
<namePart type="family">Ghannay</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>European Language Resources Association</publisher>
<place>
<placeTerm type="text">Marseille, France</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Online news consumption plays an important role in shaping the political opinions of citizens. The news is often served by recommendation algorithms, which adapt content to users’ preferences. Such algorithms can lead to political polarization as the societal effects of the recommended content and recommendation design are disregarded. We posit that biases appear, at least in part, due to a weak entanglement between natural language processing and recommender systems, both processes yet at work in the diffusion and personalization of online information. We assume that both diversity and acceptability of recommended content would benefit from such a synergy. We discuss the limitations of current approaches as well as promising leads of opinion-mining integration for the political news recommendation process.</abstract>
<identifier type="citekey">dufraisse-etal-2022-dont</identifier>
<location>
<url>https://aclanthology.org/2022.politicalnlp-1.11</url>
</location>
<part>
<date>2022-06</date>
<extent unit="page">
<start>79</start>
<end>85</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Don’t Burst Blindly: For a Better Use of Natural Language Processing to Fight Opinion Bubbles in News Recommendations
%A Dufraisse, Evan
%A Treuillier, Célina
%A Brun, Armelle
%A Tourille, Julien
%A Castagnos, Sylvain
%A Popescu, Adrian
%Y Afli, Haithem
%Y Alam, Mehwish
%Y Bouamor, Houda
%Y Casagran, Cristina Blasi
%Y Boland, Colleen
%Y Ghannay, Sahar
%S Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F dufraisse-etal-2022-dont
%X Online news consumption plays an important role in shaping the political opinions of citizens. The news is often served by recommendation algorithms, which adapt content to users’ preferences. Such algorithms can lead to political polarization as the societal effects of the recommended content and recommendation design are disregarded. We posit that biases appear, at least in part, due to a weak entanglement between natural language processing and recommender systems, both processes yet at work in the diffusion and personalization of online information. We assume that both diversity and acceptability of recommended content would benefit from such a synergy. We discuss the limitations of current approaches as well as promising leads of opinion-mining integration for the political news recommendation process.
%U https://aclanthology.org/2022.politicalnlp-1.11
%P 79-85
Markdown (Informal)
[Don’t Burst Blindly: For a Better Use of Natural Language Processing to Fight Opinion Bubbles in News Recommendations](https://aclanthology.org/2022.politicalnlp-1.11) (Dufraisse et al., PoliticalNLP 2022)
ACL