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        <title>OK Boomer: Probing the socio-demographic Divide in Echo Chambers</title>
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        <namePart type="given">Henri-Jacques</namePart>
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            <title>Proceedings of the Tenth International Workshop on Natural Language Processing for Social Media</title>
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            <namePart type="given">Lun-Wei</namePart>
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            <namePart type="given">Yu-Che</namePart>
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    <abstract>Social media platforms such as Twitter or Reddit have become an integral part in political opinion formation and discussions, accompanied by potential echo chamber forming. In this paper, we examine the relationships between the interaction patterns, the opinion polarity, and the socio-demographic characteristics in discussion communities on Reddit. On a dataset of over 2 million posts coming from over 20k users, we combine network community detection algorithms, reliable stance polarity annotations, and NLP-based socio-demographic estimations, to identify echo chambers and understand their properties at scale. We show that the separability of the interaction communities is more strongly correlated to the relative socio-demographic divide, rather than the stance polarity gap size. We further demonstrate that the socio-demographic classifiers have a strong topical bias and should be used with caution, merely for the relative community difference comparisons within a topic, rather than for any absolute labeling.</abstract>
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%0 Conference Proceedings
%T OK Boomer: Probing the socio-demographic Divide in Echo Chambers
%A Geiss, Henri-Jacques
%A Sakketou, Flora
%A Flek, Lucie
%Y Ku, Lun-Wei
%Y Li, Cheng-Te
%Y Tsai, Yu-Che
%Y Wang, Wei-Yao
%S Proceedings of the Tenth International Workshop on Natural Language Processing for Social Media
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F geiss-etal-2022-ok
%X Social media platforms such as Twitter or Reddit have become an integral part in political opinion formation and discussions, accompanied by potential echo chamber forming. In this paper, we examine the relationships between the interaction patterns, the opinion polarity, and the socio-demographic characteristics in discussion communities on Reddit. On a dataset of over 2 million posts coming from over 20k users, we combine network community detection algorithms, reliable stance polarity annotations, and NLP-based socio-demographic estimations, to identify echo chambers and understand their properties at scale. We show that the separability of the interaction communities is more strongly correlated to the relative socio-demographic divide, rather than the stance polarity gap size. We further demonstrate that the socio-demographic classifiers have a strong topical bias and should be used with caution, merely for the relative community difference comparisons within a topic, rather than for any absolute labeling.
%R 10.18653/v1/2022.socialnlp-1.8
%U https://aclanthology.org/2022.socialnlp-1.8
%U https://doi.org/10.18653/v1/2022.socialnlp-1.8
%P 83-105

Markdown (Informal)

[OK Boomer: Probing the socio-demographic Divide in Echo Chambers](https://aclanthology.org/2022.socialnlp-1.8) (Geiss et al., SocialNLP 2022)

• OK Boomer: Probing the socio-demographic Divide in Echo Chambers (Geiss et al., SocialNLP 2022)

ACL

• Henri-Jacques Geiss, Flora Sakketou, and Lucie Flek. 2022. OK Boomer: Probing the socio-demographic Divide in Echo Chambers. In Proceedings of the Tenth International Workshop on Natural Language Processing for Social Media, pages 83–105, Seattle, Washington. Association for Computational Linguistics.