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        <title>Investigating Online Community Engagement through Stancetaking</title>
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    <name type="personal">
        <namePart type="given">Jai</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Suzanne</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
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            <namePart type="given">Houda</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Much work has explored lexical and semantic variation in online communities, and drawn connections to community identity and user engagement patterns. Communities also express identity through the sociolinguistic concept of stancetaking. Large-scale computational work on stancetaking has explored community similarities in their preferences for stance markers – words that serve to indicate aspects of a speaker’s stance – without considering the stance-relevant properties of the contexts in which stance markers are used. We propose representations of stance contexts for 1798 Reddit communities and show how they capture community identity patterns distinct from textual or marker similarity measures. We also relate our stance context representations to broader inter- and intra-community engagement patterns, including cross-community posting patterns and social network properties of communities. Our findings highlight the strengths of using rich properties of stance as a way of revealing community identity and engagement patterns in online multi-community spaces.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.387</identifier>
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        <date>2023-12</date>
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            <start>5814</start>
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%0 Conference Proceedings
%T Investigating Online Community Engagement through Stancetaking
%A Aggarwal, Jai
%A Diep, Brian
%A Watson, Julia
%A Stevenson, Suzanne
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F aggarwal-etal-2023-investigating
%X Much work has explored lexical and semantic variation in online communities, and drawn connections to community identity and user engagement patterns. Communities also express identity through the sociolinguistic concept of stancetaking. Large-scale computational work on stancetaking has explored community similarities in their preferences for stance markers – words that serve to indicate aspects of a speaker’s stance – without considering the stance-relevant properties of the contexts in which stance markers are used. We propose representations of stance contexts for 1798 Reddit communities and show how they capture community identity patterns distinct from textual or marker similarity measures. We also relate our stance context representations to broader inter- and intra-community engagement patterns, including cross-community posting patterns and social network properties of communities. Our findings highlight the strengths of using rich properties of stance as a way of revealing community identity and engagement patterns in online multi-community spaces.
%R 10.18653/v1/2023.findings-emnlp.387
%U https://aclanthology.org/2023.findings-emnlp.387
%U https://doi.org/10.18653/v1/2023.findings-emnlp.387
%P 5814-5830

Markdown (Informal)

[Investigating Online Community Engagement through Stancetaking](https://aclanthology.org/2023.findings-emnlp.387) (Aggarwal et al., Findings 2023)

• Investigating Online Community Engagement through Stancetaking (Aggarwal et al., Findings 2023)

ACL

• Jai Aggarwal, Brian Diep, Julia Watson, and Suzanne Stevenson. 2023. Investigating Online Community Engagement through Stancetaking. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5814–5830, Singapore. Association for Computational Linguistics.