Stance Detection in COVID-19 Tweets

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pages 1596–1611 August 1–6, 2021. ©2021 Association for Computational Linguistics 1596 Stance Detection in COVID-19 Tweets Kyle Glandt♣ Sarthak Khanal♣ Yingjie Li♦ Doina Caragea♣ Cornelia Caragea♦ ♣Computer Science, Kansas State University ♦Computer Science, University of Illinois at Chicago {kglandt,sarthakk,dcaragea}@ksu.edu {yli300,cornelia}@uic.edu Abstract The prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of stance detection data on social media sites, as users turn to social media to share their views regarding various issues related to the pandemic, e.g. stay at home mandates and wearing face masks when out in public. We set out to make use of this data by collect- ing the stance expressed by Twitter users, with respect to topics revolving around the pan- demic. We annotate a new stance detection dataset, called COVID-19-Stance. Using this newly annotated dataset, we train several es- tablished stance detection models to ascertain a baseline performance for this specific task. To further improve the performance, we em- ploy self-training and domain adaptation ap- proaches to take advantage of large amounts of unlabeled data and existing stance detec- tion datasets. The dataset, code, and other re- sources are available on GitHub.1 1 Introduction We live in unprecedented times caused by a global COVID-19 pandemic, which has forced major changes in our daily lives. Given the developments concerning COVID-19, communities and govern- ments need to take appropriate action to mitigate the effects of the novel coronavirus, which is at the root of the pandemic. For example, states in the United States that have imposed strict social dis- tancing mandates were able to slow the growth of the virus within their communities (Courtemanche et al., 2020). For such measures to work, how- ever, it is important that the public fully adhere to these guidelines and mandates. “Pandemic fatigue,” or when people become tired of pandemic man- dates and begin to ease in adherence, can lead to 1https://github.com/kglandt/ stance-detection-in-covid-19-tweets resurgences of the novel coronavirus (Feuer and Rattner, 2020). To reduce the spread of COVID-19, it is essential to understand the public’s opinion on the various initiatives, such as stay at home orders, wearing a face mask in public, school closures, etc. Understanding how the public feels about these mandates could help health officials better estimate the expected efficacy of their mandates, as well as detect pandemic fatigue before it leads to a serious resurgence of the virus. In the era of Web 2.0, and especially during a pandemic in which people often resort to online communications, social media platforms provide an astounding amount of data relating to the stance and views held by various populations with respect to a variety of current and important topics. How- ever, the total amount of data that is being gener- ated each second makes it impossible for humans alone to fully make use of them. Fortunately, recent developments in deep learning have yielded state- of-the-art performance …

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