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        <title>Unsupervised stance detection for social media discussions: A generic baseline</title>
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    <name type="personal">
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            <title>Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Yvette</namePart>
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    <abstract>With the ever-growing use of social media to express opinions on the national and international stage, unsupervised methods of stance detection are increasingly important to handle the task without costly annotation of data. The current unsupervised state-of-the-art models are designed for specific network types, either homophilic or heterophilic, and they fail to generalize to both. In this paper, we first analyze the generalization ability of recent baselines to these two very different network types. Then, we conduct extensive experiments with a baseline model based on text embeddings propagated with a graph neural network that generalizes well to heterophilic and homophilic networks. We show that it outperforms, on average, other state-of-the-art methods across the two network types. Additionally, we show that combining textual and network information outperforms using text only, and that the language model size has only a limited impact on the model performance.</abstract>
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%0 Conference Proceedings
%T Unsupervised stance detection for social media discussions: A generic baseline
%A Sutter, Maia
%A Gourru, Antoine
%A Trabelsi, Amine
%A Largeron, Christine
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F sutter-etal-2024-unsupervised
%X With the ever-growing use of social media to express opinions on the national and international stage, unsupervised methods of stance detection are increasingly important to handle the task without costly annotation of data. The current unsupervised state-of-the-art models are designed for specific network types, either homophilic or heterophilic, and they fail to generalize to both. In this paper, we first analyze the generalization ability of recent baselines to these two very different network types. Then, we conduct extensive experiments with a baseline model based on text embeddings propagated with a graph neural network that generalizes well to heterophilic and homophilic networks. We show that it outperforms, on average, other state-of-the-art methods across the two network types. Additionally, we show that combining textual and network information outperforms using text only, and that the language model size has only a limited impact on the model performance.
%U https://aclanthology.org/2024.eacl-long.107
%P 1782-1792

Markdown (Informal)

[Unsupervised stance detection for social media discussions: A generic baseline](https://aclanthology.org/2024.eacl-long.107) (Sutter et al., EACL 2024)

• Unsupervised stance detection for social media discussions: A generic baseline (Sutter et al., EACL 2024)

ACL

• Maia Sutter, Antoine Gourru, Amine Trabelsi, and Christine Largeron. 2024. Unsupervised stance detection for social media discussions: A generic baseline. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1782–1792, St. Julian’s, Malta. Association for Computational Linguistics.