Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China

Abstract

The outbreak of Corona Virus Disease 2019 (COVID-19) is a grave global public health emergency. Nowadays, social media has become the main channel through which the public can obtain information and express their opinions and feelings. This study explored public opinion in the early stages of COVID-19 in China by analyzing Sina-Weibo (a Twitter-like microblogging system in China) texts in terms of space, time, and content. Temporal changes within one-hour intervals and the spatial distribution of COVID-19-related Weibo texts were analyzed. Based on the latent Dirichlet allocation model and the random forest algorithm, a topic extraction and classification model was developed to hierarchically identify seven COVID-19-relevant topics and 13 sub-topics from Weibo texts. The results indicate that the number of Weibo texts varied over time for different topics and sub-topics corresponding with the different developmental stages of the event. The spatial distribution of COVID-19-relevant Weibo was mainly concentrated in Wuhan, Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta, and the Chengdu-Chongqing urban agglomeration. There is a synchronization between frequent daily discussions on Weibo and the trend of the COVID-19 outbreak in the real world. Public response is very sensitive to the epidemic and significant social events, especially in urban agglomerations with convenient transportation and a large population. The timely dissemination and updating of epidemic-related information and the popularization of such information by the government can contribute to stabilizing public sentiments. However, the surge of public demand and the hysteresis of social support demonstrated that the allocation of medical resources was under enormous pressure in the early stage of the epidemic. It is suggested that the government should strengthen the response in terms of public opinion and epidemic prevention and exert control in key epidemic areas, urban agglomerations, and transboundary areas at the province level. In controlling the crisis, accurate response countermeasures should be formulated following public help demands. The findings can help government and emergency agencies to better understand the public opinion and sentiments towards COVID-19, to accelerate emergency responses, and to support post-disaster management.

Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index

“…In addition, there was a significant difference between the east and the west, with coastal provinces paying more attention to the epidemic than inland provinces. This finding was consistent with the research results of Han et al [ 25 ] and Sun et al [ 28 ]. As the epicenter of the COVID-19 outbreak, Wuhan, Hubei is undoubtedly a hot spot of public concern [ 25 ].…”

“…This finding was consistent with the research results of Han et al [ 25 ] and Sun et al [ 28 ]. As the epicenter of the COVID-19 outbreak, Wuhan, Hubei is undoubtedly a hot spot of public concern [ 25 ]. Most other regions such as the Beijing-Tianjin-Hebei and Guangdong, Jiangsu, Shandong, Zhejiang, and other coastal areas have denser populations, larger internet user bases, and higher economic development levels.…”

“…The BDI temporal trend showed that the changes in the BDI corresponded to COVID-19 news and major events, similar to other studies [ 25 ]. On December 30, 2019, Wuhan Municipal Health Commission issued the “Urgent Notice on treating pneumonia of unknown cause.” On this day, the expert team of the National Health Commission of the People’s Republic of China arrived in Wuhan to formally intervene in the investigation, which was the reason for the first small wave of the BDI.…”

Track Iran's national COVID-19 response committee’s major concerns using two-stage unsupervised topic modeling

“…These studies indicate that on-time response and answering the questions raised in the web promptly decrease the probability of misinformation dissemination in social media. Furthermore, governments and disease control centers can fight rumors and conspiracy theory posts by active monitoring of social media [ 71 ]. Utilizing text mining techniques to extract health-related information for disease surveillance, misinformation detection, and public concern detection is widely studied; however, analyzing the concerns reflected in the news is not studied well.…”

The Method of How to Predict Weibo Users’ Recovery Experience on the Weekend Based on Weibo Big Data

“…Many researchers have already conducted a series of psychometric studies using Weibo as a source of data [28]. As a result, several highquality psychological researches were successfully achieved from the perspective of predicting individual personality [27,29], accessing individuals' mental health status [30,31], and predicting group social state of mind [32][33][34][35][36].…”