Abstract
During the COVID-19 pandemic, ‘Chinese Virus’ emerged as a controversial term for coronavirus. To some, it may seem like a neutral term referring to the physical origin of the virus. To many others, however, the term is in fact attaching ethnicity to the virus. While both arguments have justifications of their own, quantitative analysis of how these terms are being used in real life is lacking. In this paper, we attempt to fill this gap with fine-grained analysis. We find that tweets with a controversial term can be easily distinguished from those with neutral terms using state-of-the-art classifiers, that they cover different substantive topics, and that they possess different linguistic features reflecting sentiment and psychological states. All evidence suggests that the real life use of the controversial terms is distinctively different from that of the neutral terms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
The streaming keywords for data streaming purposes.
- 7.
\(\mathrm {C}_\mathrm{{v}}\) is a performance measure based on a sliding window, one-set segmentation of the top words and an indirect confirmation measure that uses normalized pointwise mutual information (NPMI) and the cosine similarity.
- 8.
- 9.
The titling of this topic is rather difficult, as most keywords do not contain significant meaning associated with COVID-19. We finally choose ‘Stay Home” as the topic because a keyword ‘home” is in the topic.
- 10.
References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Chatzakou, D., Vakali, A.: Harvesting opinions and emotions from social media textual resources. IEEE Internet Comput. 19(4), 46–50 (2015)
Chen, L., et al.: A social media study on the associations of flavored electronic cigarettes with health symptoms: observational study. J. Med. Internet Res. 22(6), e17496 (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2018). arXiv:1810.04805
Gunsch, M.A., Brownlow, S., Haynes, S.E., Mabe, Z.: Differential forms linguistic content of various of political advertising. J. Broadcasting Electron. Media 44(1), 27–42 (2000)
Lukasik, M., Srijith, P., Vu, D., Bontcheva, K., Zubiaga, A., Cohn, T.: Hawkes processes for continuous time sequence classification: an application to rumour stance classification in Twitter. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 393–398 (2016)
Lyu, H., Chen, L., Wang, Y., Luo, J.: Sense and Sensibility: Characterizing social media users regarding the use of controversial terms for covid-19. IEEE Trans. Big Data 1 (2020)
McClelland, D.C.: Inhibited power motivation and high blood pressure in men. J. Abnormal Psychol. 88(2), 182 (1979)
Mouthami, K., Devi, K.N., Bhaskaran, V.M.: Sentiment analysis and classification based on textual reviews. In: 2013 International Conference on Information Communication and Embedded Systems (ICICES), pp. 271–276. IEEE (2013)
Müller, M., Salathé, M., Kummervold, P.E.: COVID-Twitter-BERT: A natural language processing model to analyse covid-19 content on Twitter. arXiv preprint arXiv:2005.07503 (2020)
Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The development and psychometric properties of LIWC2015. Technical report (2015)
Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)
Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with Twitter: What 140 characters reveal about political sentiment. In: Fourth International AAAI Conference on Weblogs and Social Media (2010)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, pp. 5754–5764 (2019)
Yu, B., Kaufmann, S., Diermeier, D.: Exploring the characteristics of opinion expressions for political opinion classification. In: Proceedings of the 2008 International Conference on Digital Government Research. pp. 82–91. Digital Government Society of North America (2008)
Zhang, D., Li, S., Wang, H., Zhou, G.: User classification with multiple textual perspectives. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2112–2121 (2016)
Zhang, X., Lyu, H., Luo, J.: What contributes to a crowdfunding campaign’s success? Evidence and analyses from GoFundMe data. arXiv preprint arXiv:2001.05446 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, L., Lyu, H., Yang, T., Wang, Y., Luo, J. (2021). Fine-Grained Analysis of the Use of Neutral and Controversial Terms for COVID-19 on Social Media. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2021. Lecture Notes in Computer Science(), vol 12720. Springer, Cham. https://doi.org/10.1007/978-3-030-80387-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-80387-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80386-5
Online ISBN: 978-3-030-80387-2
eBook Packages: Computer ScienceComputer Science (R0)