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Study on the Evolution of Public Opinion on Public Health Events

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Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2023)

Abstract

It is very important to properly handle public emergencies, such as accident disasters, public health events and social security events, and understanding public opinion on public emergencies and its evolution is necessary to deal with the public emergencies. In this paper, we focus on nine public health events related to COVID-19, and explore the evolution of public opinion on these events. Specifically, we first collect information of public opinion on an event from Sina Weibo, including posts, comments. Based on the collected data, commenting networks are constructed. After that, we design a method to explore the evolution of public opinion on these events by observing and analyzing the evolution of commenting networks, including the changes in the number, emotions and topics of the comments. Further, we analyze the influence of emotion on the number and the topics of the comments. Finally, we obtain some observations that can help the emergency management departments understand the evolution of public opinion on public health events, and developing emergency plans to guide and control it.

Supported by the National Natural Science Foundation of China (No. 61802034), Sichuan Science and Technology Programs (Nos. 2021YFG0333 and 2022YFQ0017).

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References

  1. Alalwan, A.A., Rana, N.P., Dwivedi, Y.K., Algharabat, R.: Social media in marketing: a review and analysis of the existing literature. Telematics Inform. 34(7), 1177–1190 (2017). https://doi.org/10.1016/j.tele.2017.05.008

    Article  Google Scholar 

  2. Alam, F., Ofli, F., Imran, M.: Processing social media images by combining human and machine computing during crises. Int. J. Hum.-Comput. Interact. 34(4, SI), 311–327 (2018). https://doi.org/10.1080/10447318.2018.1427831

  3. An, L., Zhou, W., Ou, M., Li, G., Yu, C., Wang, X.: Measuring and profiling the topical influence and sentiment contagion of public event stakeholders. Int. J. Inf. Manag. 58, 102327 (2021). https://doi.org/10.1016/j.ijinfomgt.2021.102327

    Article  Google Scholar 

  4. Bai, H., Yu, G.: A weibo-based approach to disaster informatics: incidents monitor in post-disaster situation via weibo text negative sentiment analysis. Nat. Hazards 83(2), 1177–1196 (2016). https://doi.org/10.1007/s11069-016-2370-5

    Article  Google Scholar 

  5. BosonNLP: Website (2014). https://finance.sina.com.cn/stock/usstock/c/2022-06-01/doc-imizirau6022250.shtml

  6. Cai, M., Luo, H., Cui, Y.: A study on the topic-sentiment evolution and diffusion in time series of public opinion derived from emergencies. Complexity 2021 (2021). https://doi.org/10.1155/2021/2069010

  7. Castillo, C., Mendoza, M., Poblete, B.: Predicting information credibility in time-sensitive social media. Internet Res. 23(5), 560–588 (2013). https://doi.org/10.1108/IntR-05-2012-0095

    Article  Google Scholar 

  8. Chen, M., Du, W.: The predicting public sentiment evolution on public emergencies under deep learning and internet of things. J. Supercomput. 79(6), 6452–6470 (2023). https://doi.org/10.1007/s11227-022-04900-x

    Article  MathSciNet  Google Scholar 

  9. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/N19-1423

  10. Eckhardt, D., Leiras, A., Tavares Thome, A.M.: Using social media for economic disaster evaluation: a systematic literature review and real case application. Nat. Hazards Rev. 23(1), 05021020 (2022). https://doi.org/10.1061/(ASCE)NH.1527-6996.0000539

  11. Weibo released the financial report for the first quarter of 2022 (2022). https://finance.sina.com.cn/stock/usstock/c/2022-06-01/doc-imizirau6022250.shtml

  12. Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005). https://doi.org/10.1016/j.neunet.2005.06.042

    Article  Google Scholar 

  13. Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Valencia, Spain, pp. 427–431. Association for Computational Linguistics (2017)

    Google Scholar 

  14. Kwon, K.H., Bang, C.C., Egnoto, M., Rao, H.R.: Social media rumors as improvised public opinion: semantic network analyses of twitter discourses during Korean saber rattling 2013. Asian J. Commun. 26(3), 201–222 (2016). https://doi.org/10.1080/01292986.2015.1130157

    Article  Google Scholar 

  15. Li, L., Wang, Z., Zhang, Q., Wen, H.: Effect of anger, anxiety, and sadness on the propagation scale of social media posts after natural disasters. Inf. Process. Manag. 57(6), 102313 (2020). https://doi.org/10.1016/j.ipm.2020.102313

    Article  Google Scholar 

  16. Luo, H., Meng, X., Zhao, Y., Cai, M.: Exploring the impact of sentiment on multi-dimensional information dissemination using COVID-19 data in china. Comput. Hum. Behav. 144, 107733 (2023). https://doi.org/10.1016/j.chb.2023.107733

    Article  Google Scholar 

  17. Maddock, J., Starbird, K., Al-Hassani, H.J., Sandoval, D.E., Orand, M., Mason, R.M.: Characterizing online rumoring behavior using multi-dimensional signatures. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW 2015, pp. 228–241. Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2675133.2675280

  18. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality (2013)

    Google Scholar 

  19. Oh, O., Agrawal, M., Rao, H.R.: Community intelligence and social media services: a rumor theoretic analysis of tweets during social crises. MIS Q. 37(2), 407–120 (2013). https://doi.org/10.25300/MISQ/2013/37.2.05

  20. Olteanu, A., Castillo, C., Diaz, F., Vieweg, S.: Crisislex: a lexicon for collecting and filtering microblogged communications in crises. In: Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014, vol. 8, pp. 376–385 (2014). https://doi.org/10.1609/icwsm.v8i1.14538

  21. Pelen, N.N., Golgeli, M.: Vector-borne disinformation during disasters and emergencies. Physica A Stat. Mech. Appl. 596, 127157 (2022). https://doi.org/10.1016/j.physa.2022.127157

  22. Simon, T., Goldberg, A., Adini, B.: Socializing in emergencies-a review of the use of social media in emergency situations. Int. J. Inf. Manag. 35(5), 609–619 (2015). https://doi.org/10.1016/j.ijinfomgt.2015.07.001

    Article  Google Scholar 

  23. Son, J., Lee, H.K., Jin, S., Lee, J.: Content features of tweets for effective communication during disasters: a media synchronicity theory perspective. Int. J. Inf. Manag. 45, 56–68 (2019). https://doi.org/10.1016/j.ijinfomgt.2018.10.012

    Article  Google Scholar 

  24. Stieglitz, S., Mirbabaie, M., Ross, B., Neuberger, C.: Social media analytics - challenges in topic discovery, data collection, and data preparation. Int. J. Inf. Manag. 39, 156–168 (2018). https://doi.org/10.1016/j.ijinfomgt.2017.12.002

    Article  Google Scholar 

  25. Yang, T., et al.: Social media big data mining and spatio-temporal analysis on public emotions for disaster mitigation. ISPRS Int. J. Geo-Inf. 8(1), 29 (2019). https://doi.org/10.3390/ijgi8010029

    Article  Google Scholar 

  26. Yigitcanlar, T., et al.: Detecting natural hazard-related disaster impacts with social media analytics: the case of Australian states and territories. Sustainability 14(2), 810 (2022). https://doi.org/10.3390/su14020810

    Article  Google Scholar 

  27. Zahra, K., Imran, M., Ostermann, F.O.: Automatic identification of eyewitness messages on twitter during disasters. Inf. Process. Manag. 57(1), 102107 (2020). https://doi.org/10.1016/j.ipm.2019.102107

    Article  Google Scholar 

  28. Zhang, D., Zhou, L., Kehoe, J.L., Kilic, I.Y.: What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews. J. Manag. Inf. Syst. 33(2), 456–481 (2016). https://doi.org/10.1080/07421222.2016.1205907

    Article  Google Scholar 

  29. Zhang, T., Cheng, C.: Temporal and spatial evolution and influencing factors of public sentiment in natural disasters-a case study of typhoon haiyan. ISPRS Int. J. Geo-Inf. 10(5), 299 (2021). https://doi.org/10.3390/ijgi10050299

    Article  Google Scholar 

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Liu, Y., Hu, Y., Yue, X. (2024). Study on the Evolution of Public Opinion on Public Health Events. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2013. Springer, Singapore. https://doi.org/10.1007/978-981-99-9640-7_17

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  • DOI: https://doi.org/10.1007/978-981-99-9640-7_17

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