Detecting Vaccine Skepticism on Twitter Using Heterogeneous Information Networks
| العنوان: | Detecting Vaccine Skepticism on Twitter Using Heterogeneous Information Networks |
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| المؤلفون: | Tim Kreutz, Walter Daelemans |
| المصدر: | Natural Language Processing and Information Systems ISBN: 9783031084720 Lecture notes in computer science |
| بيانات النشر: | Springer International Publishing, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Computer. Automation |
| الوصف: | Identifying social media users who are skeptical of the COVID-19 vaccine is an important step in understanding and refuting negative stance taking on vaccines. While previous work on Twitter data places individual messages or whole communities as their focus, this paper aims to detect stance at the user level. We develop a system that classifies Dutch Twitter users, incorporating not only the texts that users produce, but also their actions in the form of following and retweeting. These heterogeneous data are modelled in a graph structure. Graph Convolutional Networks are trained to learn whether user nodes belong to the skeptical or non-skeptical group. Results show that all types of information are used by the model, and that especially user biographies, follows and retweets improve the predictions. On a test set of unseen users, performance declines somewhat, which is expected considering these users tweeted less and had fewer connections in the graph on average. To consider multiple degrees of vaccine skepticism, the test set was annotated with more fine-grained labels and the model was repurposed to do multiclass classification. While the model trained on binary labels was unsuited for this additional task, heterogeneous information networks were found useful to both accurately model and visualize complex user behaviors. |
| ردمك: | 978-3-031-08472-0 |
| تدمد: | 0302-9743 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25bfb20925b1b663f102a2b15b9f4060 https://doi.org/10.1007/978-3-031-08473-7_34 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....25bfb20925b1b663f102a2b15b9f4060 |
| قاعدة البيانات: | OpenAIRE |
| ردمك: | 9783031084720 |
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| تدمد: | 03029743 |