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English Information

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Authors
# Name
1 Karin Becker(karin.becker@inf.ufrgs.br)
2 Andre Sousa(mediote90@gmail.com)

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Reference
# Reference
1 Angelov, D. (2020). Top2vec: Distributed representations of topics. arXiv preprint arXiv:2008.09470.
2 Cascini, F., Pantovic, A., Al-Ajlouni, Y., Failla, G., and Ricciardi, W. (2021). Attitudes, acceptance and hesitancy among the general population worldwide to receive the COVID-19 vaccines and their contributing factors: A systematic review. EClinicalMedicine, 40.
3 de Sousa, A. and Becker, K. (2021). Pro/anti-vaxxers in brazil: a temporal analysis of covid vaccination stance in twitter. In Anais do IX Symposium on Knowledge Discovery, Mining and Learning, pages 105–112, Porto Alegre, RS, Brasil. SBC.
4 Ebeling, R., Cordova, C., Nobre, J. C., and Becker, K. (2022). Analysis of the influence of political polarization in the vaccination stance: the brazilian covid-19 scenario. In Proc. of the 15th Intl. Conference on Web and Social Media (ICWSM).
5 Garcia, K. and Berton, L. (2021). Topic detection and sentiment analysis in twitter content related to covid-19 from brazil and the usa. Applied Soft Computing, 101:107057.
6 Grootendorst, M. (2020). Bertopic: Leveraging bert and c-tf-idf to create easily interpretable topics. https://doi.org/10.5281/zenodo.4381785.
7 Hornsey, M. J., Harris, E. A., and Fielding, K. S. (2018). The psychological roots of antivaccination attitudes: A 24-nation investigation. Health Psychology, 37(4):307–315.
8 Hotez, P. (2021). Covid vaccines: time to confront anti-vax aggression. Nature, 592(7856):661.
9 Huangfu, L., Mo, Y., Zhang, P., Zeng, D. D., and He, S. (2022). Covid-19 vaccine tweets after vaccine rollout: Sentiment–based topic modeling. J Med Internet Res, 24(2):e31726.
10 Liu, J., Nie, H., Li, S., Chen, X., Cao, H., Ren, J., Lee, I., and Xia, F. (2021). Tracing the pace of covid-19 research: Topic modeling and evolution. Big Data Research, 25:100236.
11 Ma, P., Zeng-Treitler, Q., and Nelson, S. J. (2020). Use of two topic modeling methods to investigate covid vaccine hesitancy. In Proc. of the 14th Intl. Conf. on ICT, Society, and Human Beings (ICT2021), volume 14, pages 130–140.
12 Oliveira, F. B., Haque, A., Mougouei, D., Evans, S., Sichman, J. S., and Singh, M. P. (2022). Investigating the emotional response to covid-19 news on twitter: A topic modeling and emotion classification approaches. IEEE Access, 10:16883–16897.
13 Omer, S. and et alli (2021). Promoting COVID-19 vaccine acceptance: recommendations from the Lancet Commission on Vaccine Refusal, Acceptance, and Demand in the USA. The Lancet, 398(10317):2186–2192.
14 Solis Arce, J. and et alli (2021). COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. Nature Medicine, 27(8):1385–1394.
15 Song, K., Tan, X., Qin, T., Lu, J., and Liu, T.-Y. (2020). Mpnet: Masked and permuted pre-training for language understanding. arXiv preprint arXiv:2004.09297.
16 Tao, G., Miao, Y., and Ng, S. (2020). COVID-19 topic modeling and visualization. In 24th Intl. Conf. on Information Visualisation (IV), pages 734–739. IEEE.
17 Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., and Zhu, T. (2020). Public discourse and sentiment during the covid 19 pandemic: Using latent dirichlet allocation for topic modeling on twitter. PLOS ONE, 15(9):1–12.