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COVID-19-Related Communication on Twitter: Analysis of the Croatian and Polish Attitudes

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 216)

Abstract

In this paper, we analyze and compare Croatian and Polish Twitter datasets. After collecting tweets related to COVID-19 in the period from 20.01.2020 until 01.07.2020, we automatically annotated positive, negative, and neutral tweets with a simple method, and then used a classifier to annotate the dataset again. To interpret the data, the total number as well as the number of positive and negative tweets are plotted through time for Croatian and Polish tweets. The positive/negative fluctuations in the visualizations are explained in the context of certain events, such as the lockdowns, Easter, and parliamentary elections. In the last step, we analyze tokens by extracting the most frequently occurring tokens in positive or negative tweets and calculating the positive to negative (and reverse) ratios.

Keywords

  • COVID-19
  • Twitter
  • Social media
  • Sentiment analysis
  • NLP

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References

  1. Beigi G, Hu X, Maciejewski R, Liu H (2016) An overview of sentiment analysis in social media and its applications in disaster relief. In: Sentiment analysis and ontology engineering. Springer, Berlin, pp 313–340. https://doi.org/10.1007/978-3-319-30319-2_13

  2. Chandrasekaran R, Mehta V, Valkunde T, Moustakas E (2020) Topics, trends, and sentiments of tweets about the covid-19 pandemic: temporal infoveillance study. J Med Internet Res 22(10):e22–624. https://doi.org/10.2196/22624

  3. Chen Y. Skiena S (2014) Building sentiment lexicons for all major languages. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 2: short papers), pp 383–389. https://doi.org/10.3115/v1/P14-2063

  4. Jakopović H, Mikelić Preradović N (2016) Identifikacija online imidža organizacija temeljem analize sentimenata korisnički generiranog sadržaja na hrvatskim portalima. Medijska istraživanja: znanstveno-stručni časopis za novinarstvo i medije 22(2):63–82. https://doi.org/10.22572/mi.22.2.4

  5. Jarynowski A (2020) A dataset of media releases (Twitter, News and Comments, Youtube, Facebook) form Poland related to COVID-19 for open research. Zenodo. https://doi.org/10.5281/zenodo.4319813

  6. Jarynowski A, Wójta-Kempa M, Płatek D, Czopek K (2020) Attempt to understand public health relevant social dimensions of covid-19 outbreak in Poland. Available at SSRN 3570609. https://doi.org/10.2139/ssrn.3570609

  7. Lampos V, Moura S, Yom-Tov E, Cox IJ, McKendry R, Edelstein M (2020) Tracking covid-19 using online search. arXiv:2003.08086

  8. Lwin MO, Lu J, Sheldenkar A, Schulz PJ, Shin W, Gupta R, Yang Y (2020) Global sentiments surrounding the covid-19 pandemic on twitter: analysis of twitter trends. JMIR Public Health Surveill 6(2):e19–447. https://doi.org/10.2196/19447

  9. Markoski F, Zdravevski E, Ljubešić N, Gievska S (2020) Evaluation of recurrent neural network architectures for abusive language detection in cyberbullying contexts. In: Proceedings of the 17th international conference on informatics and information technologies-CIIT 2020. http://hdl.handle.net/20.500.12188/8269

  10. Martinčić-Ipšić S, Močibob E, Meštrović A (2016) Link prediction on tweets’ content. In: International cconference on information and software technologies. Springer, Berlin, Germany, pp 559–567. https://doi.org/10.1007/978-3-319-46254-7_45

  11. Martinčić-Ipšić S, Močibob E, Perc M (2017) Link prediction on twitter. PLoS One 12(7):e0181–079. https://doi.org/10.1371/journal.pone.0181079

  12. Načinović L, Perak B, Meštrović A, Martinčić-Ipšić S (2012) Identifying fear related content in croatian texts. In: Proceedings of the eighth language technologies conference, pp 153–156

    Google Scholar 

  13. Pokharel BP (2020) Twitter sentiment analysis during covid-19 outbreak in nepal. Available at SSRN 3624719. https://doi.org/10.2139/ssrn.3624719

  14. Salathé M (2018) Digital epidemiology: what is it, and where is it going? Life sciences, society and policy 14(1):1. https://doi.org/10.1186/s40504-017-0065-7

    CrossRef  Google Scholar 

  15. Strzelecki A, Azevedo A, Albuquerque A (2020) Correlation between the spread of covid-19 and the interest in personal protective measures in Poland and Portugal. In: Healthcare. Multidisciplinary Digital Publishing Institute, p 203. https://doi.org/10.3390/healthcare8030203

  16. Szmuda T, Ali S, Hetzger TV, Rosvall P, Słoniewski P (2020) Are online searches for the novel coronavirus (covid-19) related to media or epidemiology? A cross-sectional study. Int J Infect Dis. https://doi.org/10.1016/j.ijid.2020.06.028

  17. Tutek M, Sekulić I, Gombar P, Paljak I, Čulinović F, Boltužić F, Karan M, Alagić D, Šnajder J (2016) Takelab at semeval-2016 task 6: stance classification in tweets using a genetic algorithm based ensemble. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 464–468. https://doi.org/10.18653/v1/S16-1075

  18. Vicari S, Murru MF (202) One platform, a thousand worlds: on twitter irony in the early response to the covid-19 pandemic in Italy. Soc Media + Soc 6(3):2056305120948–254. https://doi.org/10.1177/2056305120948254

  19. Xue J, Chen J, Chen C, Zheng C, Li S, 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):e0239–441. https://doi.org/10.1371/journal.pone.0239441

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Acknowledgements

This work has been supported in part by the COST Action CA15109 COSTNET and by the Croatian Science Foundation under the project IP-CORONA-04-2061, “Multilayer Framework for the Information Spreading Characterization in Social Media during the COVID-19 Crisis” (InfoCoV).

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Correspondence to Andrzej Jarynowski or Ana Meštrović .

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Babić, K., Petrović, M., Beliga, S., Martinčić-Ipšić, S., Jarynowski, A., Meštrović, A. (2022). COVID-19-Related Communication on Twitter: Analysis of the Croatian and Polish Attitudes. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 216. Springer, Singapore. https://doi.org/10.1007/978-981-16-1781-2_35

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