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Title: | Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU |
Authors: | Naseem, U Khushi, M Kim, J Dunn, A |
Keywords: | social networking (online);metadata;vaccines;public healthcare;statistics;stress |
Issue Date: | 18-Jul-2021 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Naseem, U. et al. (2021) 'Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU', Proceedings of the 2021 International Joint Conference on Neural Networks, Virtual, Shenzhen, China, 18-22 July, pp. 1 - 8. doi: 10.1109/IJCNN52387.2021.9533454. |
Abstract: | Vaccines are an important public health measure, but vaccine hesitancy and refusal can create clusters of low vaccine coverage and reduce the effectiveness of vaccination programs. Social media provides an opportunity to estimate emerging risks to vaccine acceptance by including geographical location and detailing vaccine-related concerns. Methods for classifying social media posts, such as vaccine-related tweets, use language models (LMs) trained on general domain text. However, challenges to measuring vaccine sentiment at scale arise from the absence of tonal stress and gestural cues and may not always have additional information about the user, e.g., past tweets or social connections. Another challenge in LMs is the lack of commonsense knowledge that are apparent in users metadata, i.e., emoticons, positive and negative words etc. In this study, to classify vaccine sentiment tweets with limited information, we present a novel end-to-end framework consisting of interconnected components that use domain-specific LM trained on vaccine-related tweets and models commonsense knowledge into a bidirectional gated recurrent network (CK-BiGRU) with context-aware attention. We further leverage syntactical, user metadata and sentiment information to capture the sentiment of a tweet. We experimented using two popular vaccine-related Twitter datasets and demonstrate that our proposed approach outperforms state-of-the-art models in identifying pro-vaccine, anti-vaccine and neutral tweets. |
Description: | Accepted in International Joint Conference on Neural Networks (IJCNN) 2021. Cite as: arXiv:2106.09589 [cs.CL] https://doi.org/10.48550/arXiv.2106.09589 |
URI: | https://bura.brunel.ac.uk/handle/2438/27040 |
DOI: | https://doi.org/10.1109/IJCNN52387.2021.9533454 |
ISBN: | 978-1-6654-3900-8 (ebk) |
ISSN: | 2161-4393 978-1-6654-4597-9 (PoD) |
Other Identifiers: | ORCID iD: Matloob Khushi https://orcid.org/0000-0001-7792-2327 |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | Copyright © The authors 2021. Archived under a Creative Commons (CC BY) Creative Commons (https://creativecommons.org/licenses/by/4.0/) on arXiv at arXiv:2106.09589v1 [cs.CL] for this version). https://doi.org/10.48550/arXiv.2106.09589 (see: https://arxiv.org/help/license). The version of record is available at https://doi.org/10.1109/IJCNN52387.2021.9533454, copyright © 2021 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. For more information, see https://www.ieee.org/publications/rights/rights-policies.html | 924.46 kB | Adobe PDF | View/Open |
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