Please wait a minute...
Data Analysis and Knowledge Discovery  0, Vol. Issue (): 1-    DOI: 10.11925/infotech.2096-3467. 2021.0631
Current Issue | Archive | Adv Search |
Calculation of netizens' trust in government microblogs in the context of public health emergencies
An Lu,Xu Manting
(Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China) (School of Information Management, Wuhan University, China 430072, China)
Export: BibTeX | EndNote (RIS)      

[Objective] In the context of public health emergencies, it is of great value to the management departments to calculate the netizens' trust in government microblogs and explore the reasons for the change of trust.

[Methods] According to the objects of comment, the topic similarity between the comment and the microblog post, and their sentiment, the trust values of the comments of the government microblogging are calculated. Combined with the trust value of likes and that of forwarding, the comprehensive trust degrees of the netizens to the government microblogging are calculated.

[Results] Using the microblog data of COVID-19 for empirical analysis, it is found that topics related to industrial fighting epidemic can enhance the trust in government microblogging, and the role of the Chinese epidemic situation on the trust in government microblogging is affected by industrial fighting epidemic and government actions. There are great differences in the evolution trend and reasons of the trust in government microblogging in different industries.

[Limitations] Only the events and the microbloggers are considered as the objects of comments.

[Conclusions] The results reveal the changing trend and reasons of netizens' trust in government microblogging of different industries during the COVID-19, and provide data and method support for government departments to make decisions, repair and improve public trust, and guide public opinion during public health emergencies.

Key words Government Microblogging      Social Media      Public Trust      Trust Calculation      Public Emergencies      COVID-19      
Published: 10 September 2021
ZTFLH:  D63,TP391.1,C912.63  

Cite this article:

An Lu, Xu Manting. Calculation of netizens' trust in government microblogs in the context of public health emergencies . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL: 2021.0631     OR

[1] Li Gang, Zhang Ji, Mao Jin. Social Media Image Classification for Emergency Portrait[J]. 数据分析与知识发现, 2022, 6(2/3): 67-79.
[2] An Lu, Xu Manting. Measuring Online Trust in Government Microblogs in Public Health Emergencies[J]. 数据分析与知识发现, 2022, 6(1): 55-68.
[3] Xie Hao,Mao Jin,Li Gang. Sentiment Classification of Image-Text Information with Multi-Layer Semantic Fusion[J]. 数据分析与知识发现, 2021, 5(6): 103-114.
[4] Ma Yingxue,Zhao Jichang. Patterns and Evolution of Public Opinion on Weibo During Natural Disasters: Case Study of Typhoons and Rainstorms[J]. 数据分析与知识发现, 2021, 5(6): 66-79.
[5] Zhang Guobiao,Li Jie. Detecting Social Media Fake News with Semantic Consistency Between Multi-model Contents[J]. 数据分析与知识发现, 2021, 5(5): 21-29.
[6] Li He,Liu Jiayu,Li Shiyu,Wu Di,Jin Shuaiqi. Optimizing Automatic Question Answering System Based on Disease Knowledge Graph[J]. 数据分析与知识发现, 2021, 5(5): 115-126.
[7] Liu Qian, Li Chenliang. A Survey of Topic Evolution on Social Media[J]. 数据分析与知识发现, 2020, 4(8): 1-14.
[8] Li Gang, Guan Weidong, Ma Yaxue, Mao Jin. Predicting Social Media Visibility of Scholarly Articles[J]. 数据分析与知识发现, 2020, 4(8): 63-74.
[9] Nie Lei,Fu Juan,Yi Chengqi,Yang Daoling. Measuring Enterprise’s Offline Resumption with Mobile Device Positioning Data[J]. 数据分析与知识发现, 2020, 4(7): 38-49.
[10] Ying Tan,Jin Zhang,Lixin Xia. A Survey of Sentiment Analysis on Social Media[J]. 数据分析与知识发现, 2020, 4(1): 1-11.
[11] Lin Wang,Ke Wang,Jiang Wu. Public Opinion Propagation and Evolution of Public Health Emergencies in Social Media Era: A Case Study of 2018 Vaccine Event[J]. 数据分析与知识发现, 2019, 3(4): 42-52.
[12] Xiwei Wang,Duo Wang,Qingxiao Zheng,Ya’nan Wei. Information Interaction Between User and Enterprise in Online Brand Community: A Study of Virtual Reality Industry[J]. 数据分析与知识发现, 2019, 3(3): 83-94.
[13] Xiaoxiao Zhu,Zunqi Yang,Jing Liu. Construction of an Adverse Drug Reaction Extraction Model Based on Bi-LSTM and CRF[J]. 数据分析与知识发现, 2019, 3(2): 90-97.
[14] Cuiqing Jiang,Yibo Guo,Yao Liu. Constructing a Domain Sentiment Lexicon Based on Chinese Social Media Text[J]. 数据分析与知识发现, 2019, 3(2): 98-107.
[15] Gang Li,Sijing Chen,Jin Mao,Yansong Gu. Spatio-Temporal Comparison of Microblog Trending Topics on Natural Disasters[J]. 数据分析与知识发现, 2019, 3(11): 1-15.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938