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        <title>A Few Topical Tweets are Enough for Effective User Stance Detection</title>
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            <namePart type="given">Paola</namePart>
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            <namePart type="family">Tsarfaty</namePart>
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    <abstract>User stance detection entails ascertaining the position of a user towards a target, such as an entity, topic, or claim. Recent work that employs unsupervised classification has shown that performing stance detection on vocal Twitter users, who have many tweets on a target, can be highly accurate (+98%). However, such methods perform poorly or fail completely for less vocal users, who may have authored only a few tweets about a target. In this paper, we tackle stance detection for such users using two approaches. In the first approach, we improve user-level stance detection by representing tweets using contextualized embeddings, which capture latent meanings of words in context. We show that this approach outperforms two strong baselines and achieves 89.6% accuracy and 91.3% macro F-measure on eight controversial topics. In the second approach, we expand the tweets of a given user using their Twitter timeline tweets, which may not be topically relevant, and then we perform unsupervised classification of the user, which entails clustering a user with other users in the training set. This approach achieves 95.6% accuracy and 93.1% macro F-measure.</abstract>
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        <date>2021-04</date>
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%0 Conference Proceedings
%T A Few Topical Tweets are Enough for Effective User Stance Detection
%A Samih, Younes
%A Darwish, Kareem
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F samih-darwish-2021-topical
%X User stance detection entails ascertaining the position of a user towards a target, such as an entity, topic, or claim. Recent work that employs unsupervised classification has shown that performing stance detection on vocal Twitter users, who have many tweets on a target, can be highly accurate (+98%). However, such methods perform poorly or fail completely for less vocal users, who may have authored only a few tweets about a target. In this paper, we tackle stance detection for such users using two approaches. In the first approach, we improve user-level stance detection by representing tweets using contextualized embeddings, which capture latent meanings of words in context. We show that this approach outperforms two strong baselines and achieves 89.6% accuracy and 91.3% macro F-measure on eight controversial topics. In the second approach, we expand the tweets of a given user using their Twitter timeline tweets, which may not be topically relevant, and then we perform unsupervised classification of the user, which entails clustering a user with other users in the training set. This approach achieves 95.6% accuracy and 93.1% macro F-measure.
%R 10.18653/v1/2021.eacl-main.227
%U https://aclanthology.org/2021.eacl-main.227
%U https://doi.org/10.18653/v1/2021.eacl-main.227
%P 2637-2646

Markdown (Informal)

[A Few Topical Tweets are Enough for Effective User Stance Detection](https://aclanthology.org/2021.eacl-main.227) (Samih & Darwish, EACL 2021)

• A Few Topical Tweets are Enough for Effective User Stance Detection (Samih & Darwish, EACL 2021)

ACL

• Younes Samih and Kareem Darwish. 2021. A Few Topical Tweets are Enough for Effective User Stance Detection. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2637–2646, Online. Association for Computational Linguistics.