Abstract:

In response to the COVID-19, social media big data has played an important role in epidemic warning, tracking the source of infection, and public opinion monitoring, providing strong technical support for China’s epidemic prevention and control work. The paper used Sina Weibo posts related to COVID-19 hashtags as the data source, and built a BERT-CNN deep learning model to perform fine-grained and high-precision topic classificationon massive social media posts. Taking Shenzhen as a region of interest, we mined the “epidemic data bulletin” and “daily life impact” posts during the epidemic for spatial analysis. The results show that the confirmed communities and designated hospitals in Shenzhen as a whole present the characteristics of “sparse east and dense west”, and there is a strong positive spatial correlation between the number of confirmed cases and social media response. Specifically, Nanshan District, Futian District and Luohu District have more confirmed cases due to large population movements and dense transportation networks, and social media has responded more violently, and people’s lives have been greatly affected. However, Yantian District, Pingshan District and Dapeng New District showed opposite characteristics. The case study results further show that using deep learning methods to mine text information in social media is scientifically feasible for improving situational awareness and decision support during the COVID-19.

Key words: COVID-19, Sina Weibo, BERT-CNN, topic classification, situational awareness