Describing COVID-19 Pandemic by means of Tweets from Official Entities in Brazil
Resumo
In a world flooded with information, not always true, nor rarely biased, the communication of official entities assumes a key role. In this work we analyze a dataset composed of tweets from stakeholder entities regarding COVID-19 pandemic, to cite: National Agency of Sanitary Surveillance, Ministry of Health, World Health Organization and Brazilian Society of Infectious Diseases. We describe, by means of social, semantic and temporal patterns, the communication characteristics of above entities in social networks during COVID-19 pandemic. Further, we cross those patterns with key-facts occurred during pandemic. Results show that communication in social networks tend to be biased and not sufficient to comprehend the whole context. Furthermore, public entities are immature in their communication strategies in social networks.
Referências
Budnik, E., Gaputina, V., and Boguslavskaya, V. (2019). Dynamic of hashtag functions development in new media: Hashtag as an identificational mark of digital communication in social networks. In Proceedings of the XI International Scientific Conference Communicative Strategies of the Information Society, CSIS’2019, New York, NY, USA. Association for Computing Machinery.
Dai, L., Wang, H., and Liu, X. (2020). St-etm: A spatial-temporal emergency topic model for public opinion identifying in social networks. IEEE Access, 8:125659–125670.
Dejard, L., Santos, A., Junior, H., Paulino, R., Figueiredo, K., Costa, F., Vidal, D., Pires, Y., and Seruffo, M. (2021). Social and institutional presence of the presidents of the americas on social media: An analysis of the communication on twitter about covid-19. International Journal of Advanced Engineering Research and Science, 8(8):512–526.
Dimitriadis, I., Poiitis, M., Faloutsos, C., and Vakali, A. (2022). Tg-out: temporal outlier patterns detection in twitter attribute induced graphs. World Wide Web, pages 1–25.
Dimitrov, D., Baran, E., Fafalios, P., Yu, R., Zhu, X., Zloch, M., and Dietze, S. (2020). Tweetscov19 a knowledge base of semantically annotated tweets about the covid-19 pandemic. In Proceedings of the 29th ACM International Conference on Information Knowledge Management, CIKM ’20, page 2991–2998, New York, NY, USA. Association for Computing Machinery.
Duque-Rengel, V. K., Calva-Cabrera, K. D., and Márquez-Domínguez, C. (2021). Government communication on twitter during the covid-19 pandemic in ecuador. In 2021 16th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6.
Freelon, A. and S., H. (2021). Brazil’s main covid strategy is a cocktail of unproven drugs.
Iyer, R., Wong, J., Tavanapong, W., and Peterson, D. A. M. (2017). Identifying policy agenda sub-topics in political tweets based on community detection. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, page 698–705, New York, NY, USA. Association for Computing Machinery.
Kwan, J. S.-L. and Lim, K. H. (2021). Tweetcovid: A system for analyzing public sentiments and discussions about covid-19 via twitter activities. In 26th International Conference on Intelligent User Interfaces Companion, IUI ’21 Companion, page 58–60, New York, NY, USA. Association for Computing Machinery.
Leyman, I. I., Filimonov, V. V., and Ivanov, F. I. (2021). Social media and the strategy of crisis communication in covid-19 pandemic: a case study of komi republic (russia). In 2021 Communication Strategies in Digital Society Seminar (ComSDS), pages 191–195.
Li, L., Aldosery, A., Vitiugin, F., Nathan, N., Novillo-Ortiz, D., Castillo, C., and Kostkova, P. (2021). The response of governments and public health agencies to covid19 pandemics on social media: A multi-country analysis of twitter discourse. Frontiers in Public Health, 9.
Li, L., Zhang, Q., Wang, X., Zhang, J., Wang, T., Gao, T.-L., Duan, W., Tsoi, K. K.-f., and Wang, F.-Y. (2020). Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo. IEEE Transactions on Computational Social Systems, 7(2):556–562.
Miletskiy, V. P., Cherezov, D. N., and Strogetskaya, E. V. (2019). Transformations of professional political communications in the digital society (by the example of the fake news communication strategy). In 2019 Communication Strategies in Digital Society Workshop (ComSDS), pages 121–124.
Praznik, L., Srivastava, G., Mendhe, C., and Mago, V. (2019). Vertex-weighted measures for link prediction in hashtag graphs. In 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 1034–1041.
Rao, C. (1982). Diversity and dissimilarity coefficients: A unified approach. Theoretical Population Biology, 21(1):24–43.
Saude, S. (2022). Linha do tempo do Coronavírus no Brasil. [link]. [Online; accessed May-2022].
Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface, 4(15):707–719.
Walter, D. (2018). Exploring thematic diversity in news coverage and social media activity of political candidates using unsupervised machine learning.
Wang, Y., Liu, J., Huang, Y., and Feng, X. (2016). Using hashtag graph-based topic model to connect semantically-related words without co-occurrence in microblogs. IEEE Transactions on Knowledge and Data Engineering, 28(7):1919–1933.
Zafarani, R., Abbasi, M. A., and Liu, H. (2014). Social Media Mining: An Introduction. Cambridge University Press, New York, NY, USA.
Dai, L., Wang, H., and Liu, X. (2020). St-etm: A spatial-temporal emergency topic model for public opinion identifying in social networks. IEEE Access, 8:125659–125670.
Dejard, L., Santos, A., Junior, H., Paulino, R., Figueiredo, K., Costa, F., Vidal, D., Pires, Y., and Seruffo, M. (2021). Social and institutional presence of the presidents of the americas on social media: An analysis of the communication on twitter about covid-19. International Journal of Advanced Engineering Research and Science, 8(8):512–526.
Dimitriadis, I., Poiitis, M., Faloutsos, C., and Vakali, A. (2022). Tg-out: temporal outlier patterns detection in twitter attribute induced graphs. World Wide Web, pages 1–25.
Dimitrov, D., Baran, E., Fafalios, P., Yu, R., Zhu, X., Zloch, M., and Dietze, S. (2020). Tweetscov19 a knowledge base of semantically annotated tweets about the covid-19 pandemic. In Proceedings of the 29th ACM International Conference on Information Knowledge Management, CIKM ’20, page 2991–2998, New York, NY, USA. Association for Computing Machinery.
Duque-Rengel, V. K., Calva-Cabrera, K. D., and Márquez-Domínguez, C. (2021). Government communication on twitter during the covid-19 pandemic in ecuador. In 2021 16th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6.
Freelon, A. and S., H. (2021). Brazil’s main covid strategy is a cocktail of unproven drugs.
Iyer, R., Wong, J., Tavanapong, W., and Peterson, D. A. M. (2017). Identifying policy agenda sub-topics in political tweets based on community detection. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, page 698–705, New York, NY, USA. Association for Computing Machinery.
Kwan, J. S.-L. and Lim, K. H. (2021). Tweetcovid: A system for analyzing public sentiments and discussions about covid-19 via twitter activities. In 26th International Conference on Intelligent User Interfaces Companion, IUI ’21 Companion, page 58–60, New York, NY, USA. Association for Computing Machinery.
Leyman, I. I., Filimonov, V. V., and Ivanov, F. I. (2021). Social media and the strategy of crisis communication in covid-19 pandemic: a case study of komi republic (russia). In 2021 Communication Strategies in Digital Society Seminar (ComSDS), pages 191–195.
Li, L., Aldosery, A., Vitiugin, F., Nathan, N., Novillo-Ortiz, D., Castillo, C., and Kostkova, P. (2021). The response of governments and public health agencies to covid19 pandemics on social media: A multi-country analysis of twitter discourse. Frontiers in Public Health, 9.
Li, L., Zhang, Q., Wang, X., Zhang, J., Wang, T., Gao, T.-L., Duan, W., Tsoi, K. K.-f., and Wang, F.-Y. (2020). Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo. IEEE Transactions on Computational Social Systems, 7(2):556–562.
Miletskiy, V. P., Cherezov, D. N., and Strogetskaya, E. V. (2019). Transformations of professional political communications in the digital society (by the example of the fake news communication strategy). In 2019 Communication Strategies in Digital Society Workshop (ComSDS), pages 121–124.
Praznik, L., Srivastava, G., Mendhe, C., and Mago, V. (2019). Vertex-weighted measures for link prediction in hashtag graphs. In 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 1034–1041.
Rao, C. (1982). Diversity and dissimilarity coefficients: A unified approach. Theoretical Population Biology, 21(1):24–43.
Saude, S. (2022). Linha do tempo do Coronavírus no Brasil. [link]. [Online; accessed May-2022].
Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface, 4(15):707–719.
Walter, D. (2018). Exploring thematic diversity in news coverage and social media activity of political candidates using unsupervised machine learning.
Wang, Y., Liu, J., Huang, Y., and Feng, X. (2016). Using hashtag graph-based topic model to connect semantically-related words without co-occurrence in microblogs. IEEE Transactions on Knowledge and Data Engineering, 28(7):1919–1933.
Zafarani, R., Abbasi, M. A., and Liu, H. (2014). Social Media Mining: An Introduction. Cambridge University Press, New York, NY, USA.
Publicado
06/08/2023
Como Citar
SANTOS, Matheus Henrique dos; PEREIRA, Fabiola S. F..
Describing COVID-19 Pandemic by means of Tweets from Official Entities in Brazil. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 12. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 175-186.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2023.230780.
