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        <title>Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity</title>
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        <namePart type="given">Valentin</namePart>
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            <namePart type="given">Ivan</namePart>
            <namePart type="given">Vladimir</namePart>
            <namePart type="family">Meza Ruiz</namePart>
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    <abstract>The increasing polarization of online political discourse calls for computational tools that automatically detect and monitor ideological divides in social media. We introduce a minimally supervised method that leverages the network structure of online discussion forums, specifically Reddit, to detect polarized concepts. We model polarization along the dimensions of salience and framing, drawing upon insights from moral psychology. Our architecture combines graph neural networks with structured sparsity learning and results in representations for concepts and subreddits that capture temporal ideological dynamics such as right-wing and left-wing radicalization.</abstract>
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%0 Conference Proceedings
%T Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity
%A Hofmann, Valentin
%A Dong, Xiaowen
%A Pierrehumbert, Janet
%A Schuetze, Hinrich
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F hofmann-etal-2022-modeling
%X The increasing polarization of online political discourse calls for computational tools that automatically detect and monitor ideological divides in social media. We introduce a minimally supervised method that leverages the network structure of online discussion forums, specifically Reddit, to detect polarized concepts. We model polarization along the dimensions of salience and framing, drawing upon insights from moral psychology. Our architecture combines graph neural networks with structured sparsity learning and results in representations for concepts and subreddits that capture temporal ideological dynamics such as right-wing and left-wing radicalization.
%R 10.18653/v1/2022.findings-naacl.41
%U https://aclanthology.org/2022.findings-naacl.41
%U https://doi.org/10.18653/v1/2022.findings-naacl.41
%P 536-550

Markdown (Informal)

[Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity](https://aclanthology.org/2022.findings-naacl.41) (Hofmann et al., Findings 2022)

• Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity (Hofmann et al., Findings 2022)

ACL

• Valentin Hofmann, Xiaowen Dong, Janet Pierrehumbert, and Hinrich Schuetze. 2022. Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 536–550, Seattle, United States. Association for Computational Linguistics.