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        <title>Conditional Language Models for Community-Level Linguistic Variation</title>
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    <name type="personal">
        <namePart type="given">Bill</namePart>
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            <namePart type="given">David</namePart>
            <namePart type="family">Bamman</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Jurgens</namePart>
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        <name type="personal">
            <namePart type="given">Katherine</namePart>
            <namePart type="family">Keith</namePart>
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        <name type="personal">
            <namePart type="given">Brendan</namePart>
            <namePart type="family">O’Connor</namePart>
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        <name type="personal">
            <namePart type="given">Svitlana</namePart>
            <namePart type="family">Volkova</namePart>
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    <abstract>Community-level linguistic variation is a core concept in sociolinguistics. In this paper, we use conditioned neural language models to learn vector representations for 510 online communities. We use these representations to measure linguistic variation between commu-nities and investigate the degree to which linguistic variation corresponds with social connections between communities. We find that our sociolinguistic embeddings are highly correlated with a social network-based representation that does not use any linguistic input.</abstract>
    <identifier type="citekey">noble-bernardy-2022-conditional</identifier>
    <identifier type="doi">10.18653/v1/2022.nlpcss-1.9</identifier>
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%0 Conference Proceedings
%T Conditional Language Models for Community-Level Linguistic Variation
%A Noble, Bill
%A Bernardy, Jean-philippe
%Y Bamman, David
%Y Hovy, Dirk
%Y Jurgens, David
%Y Keith, Katherine
%Y O’Connor, Brendan
%Y Volkova, Svitlana
%S Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F noble-bernardy-2022-conditional
%X Community-level linguistic variation is a core concept in sociolinguistics. In this paper, we use conditioned neural language models to learn vector representations for 510 online communities. We use these representations to measure linguistic variation between commu-nities and investigate the degree to which linguistic variation corresponds with social connections between communities. We find that our sociolinguistic embeddings are highly correlated with a social network-based representation that does not use any linguistic input.
%R 10.18653/v1/2022.nlpcss-1.9
%U https://aclanthology.org/2022.nlpcss-1.9/
%U https://doi.org/10.18653/v1/2022.nlpcss-1.9
%P 59-78

Markdown (Informal)

[Conditional Language Models for Community-Level Linguistic Variation](https://aclanthology.org/2022.nlpcss-1.9/) (Noble & Bernardy, NLP+CSS 2022)

• Conditional Language Models for Community-Level Linguistic Variation (Noble & Bernardy, NLP+CSS 2022)

ACL

• Bill Noble and Jean-philippe Bernardy. 2022. Conditional Language Models for Community-Level Linguistic Variation. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS), pages 59–78, Abu Dhabi, UAE. Association for Computational Linguistics.