Abstract
The ongoing COVID-19 pandemic has posed serious threats to the world population, affecting over 219 countries with a staggering impact of over 162 million cases and 3.36 million casualties. With the availability of multiple vaccines across the globe, framing vaccination policies for effectively inoculating a country’s population against such diseases is currently a crucial task for public health agencies. Social network users post their views and opinions on vaccines publicly and these posts can be put to good use in identifying vaccine hesitancy. In this paper, a vaccine hesitancy identification approach is proposed, built on novel text feature modeling based on evolutionary computation and topic modeling. The proposed approach was experimentally validated on two standard tweet datasets – the flu vaccine dataset and UK COVID-19 vaccine tweets. On the first dataset, the proposed approach outperformed the state-of-the-art in terms of standard metrics. The proposed model was also evaluated on the UKCOVID dataset and the results are presented in this paper, as our work is the first to benchmark a vaccine hesitancy model on this dataset.
G. S. Krishnan—Work done as part of doctoral research work at HALE Lab, NITK Surathkal.
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References
Alshammari, S.M., Nielsen, R.D.: Less is more: with a 280-character limit, Twitter provides a valuable source for detecting self-reported flu cases. In: Proceedings of the 2018 International Conference on Computing and Big Data, pp. 1–6. ACM (2018)
Byrd, K., Mansurov, A., Baysal, O.: Mining Twitter data for influenza detection and surveillance. In: Proceedings of the International Workshop on Software Engineering in Healthcare Systems, pp. 43–49. ACM (2016)
Cocos, A., Fiks, A.G., Masino, A.J.: Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts. JAMIA 24(4), 813–821 (2017)
Dubé, E., Laberge, C., Guay, M., Bramadat, P., Roy, R., Bettinger, J.A.: Vaccine hesitancy: an overview. Hum. Vaccines Immunotherapeutics 9(8), 1763–1773 (2013)
Gomez, J.C., Hoskens, S., Moens, M.F.: Evolutionary learning of meta-rules for text classification. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 131–132 (2017)
Huang, X., Smith, M.C., Paul, M.J., et al.: Examining patterns of influenza vaccination in social media. In: Workshops at 31st AAAI Conference on Artificial Intelligence (2017)
Hussain, A., Tahir, A., Hussain, Z., Sheikh, Z., Gogate, M., et al.: Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and twitter towards Covid-19 vaccinations (2020)
Ignatenko, V., Koltcov, S., Staab, S., Boukhers, Z.: Fractal approach for determining the optimal number of topics in the field of topic modeling. J. Phys.: Conf. Ser. 1163 (2019)
Joshi, A., Dai, X., Karimi, S., Sparks, R., Paris, C., MacIntyre, C.R.: Shot or not: comparison of NLP approaches for vaccination behaviour detection. In: Proceedings of the 2018 EMNLP Workshop, pp. 43–47 (2018)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Li, C., Wang, H., Zhang, Z., Sun, A., Ma, Z.: Topic modeling for short texts with auxiliary word embeddings. In: ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165–174 (2016)
Moslehi, F., Haeri, A.: An evolutionary computation-based approach for feature selection. J. Ambient Intell. Hum. Comput. 1–13 (2019)
Parker, A.M., Vardavas, R., Marcum, C.S., Gidengil, C.A.: Conscious consideration of herd immunity in influenza vaccination decisions. Am. J. Prev. Med. 45(1), 118–121 (2013)
Sarker, A., et al.: Utilizing social media data for pharmacovigilance: a review. J. Biomed. Inform. 54, 202–212 (2015)
Steinskog, A., Therkelsen, J., Gambäck, B.: Twitter topic modeling by tweet aggregation. In: Proceedings of the 21st Nordic Conference on Computational Linguistics, pp. 77–86 (2017)
Wakamiya, S., Kawai, Y., Aramaki, E.: Twitter-based influenza detection afterflu peak via tweets with indirect information: text mining study. JMIR Public Health Surveillance 4(3), e65 (2018)
Yang, H., Ma, J.: How an epidemic outbreak impacts happiness: factors that worsen (vs. protect) emotional well-being during the coronavirus pandemic. Psychiatry Res. 289, 113045 (2020)
Zhao, W., et al.: A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinform. 16, S8 (2015). https://doi.org/10.1186/1471-2105-16-S13-S8
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Krishnan, G.S., Sowmya Kamath, S., Sugumaran, V. (2021). Predicting Vaccine Hesitancy and Vaccine Sentiment Using Topic Modeling and Evolutionary Optimization. In: Métais, E., Meziane, F., Horacek, H., Kapetanios, E. (eds) Natural Language Processing and Information Systems. NLDB 2021. Lecture Notes in Computer Science(), vol 12801. Springer, Cham. https://doi.org/10.1007/978-3-030-80599-9_23
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