Abstract
In this paper, by analyzing the characteristics of the news reports of the COVID-19 epidemic events, we extract the event ancestor pairs from the text, extract the relationship between the events through attention-based bidirectional LSTM, and display them in the form of EEG model, which is conducive to the analysis of the evolution of epidemic events. The method proposed in this paper provides a new idea for the evolution of network events. The constructed event map can clearly show the evolution path of network events, monitor key nodes of network events, assist relevant management departments to formulate corresponding measures, and lead the events forward in a positive way.
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Acknowledgements
This work is supported by National Key Research and Development Program of China (Project No. 2018YFC0806903), the basic work project of Ministry of public security science and technology (Project No. 2019GABJC20) and the Key Lab of Information Network Security of Ministry of Public Security C19600 (The Third Research Institute of Ministry of Public Security).
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Xie, K., Yang, T., Fan, R., Jiang, G. (2022). Analysis of Epidemic Events Based on Event Evolutionary Graph. In: Barolli, L., Yim, K., Chen, HC. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing. IMIS 2021. Lecture Notes in Networks and Systems, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-79728-7_3
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DOI: https://doi.org/10.1007/978-3-030-79728-7_3
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