Béres, Ferenc and Michaletzky, T V and Csoma, R and Benczúr, András, ifj (2023) Network embedding aided vaccine skepticism detection. APPLIED NETWORK SCIENCE, 8 (1). ISSN 2364-8228 10.1007/s41109-023-00534-x

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Abstract

We investigate automatic methods to assess COVID vaccination views in Twitter content. Vaccine skepticism has been a controversial topic of long history that has become more important than ever with the COVID-19 pandemic. Our main goal is to demonstrate the importance of network effects in detecting vaccination skeptic content. Towards this end, we collected and manually labeled vaccination-related Twitter content in the first half of 2021. Our experiments confirm that the network carries information that can be exploited to improve the accuracy of classifying attitudes towards vaccination over content classification as baseline. We evaluate a variety of network embedding algorithms, which we combine with text embedding to obtain classifiers for vaccination skeptic content. In our experiments, by using Walklets, we improve the AUC of the best classifier with no network information by. We publicly release our labels, Tweet IDs and source codes on GitHub.

Item Type:	Article
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Informatics Laboratory
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	25 Mar 2023 16:24
Last Modified:	11 Sep 2023 14:59
URI:	https://eprints.sztaki.hu/id/eprint/10510

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