Analysis of Information Dissemination and Public Opinion Based on Complex Network

2020 ◽  
Vol 09 (01) ◽  
pp. 86-93
Author(s):  
睿谦 李
Keyword(s):  
Public Opinion ◽  
Complex Network ◽  
Information Dissemination

Information Dissemination Mechanism Based on Cloud Computing Cross-Media Public Opinion Network Environment

2021 ◽  
Vol 14 (2) ◽  
pp. 70-83
Author(s):  
Ping Liu
Keyword(s):  
Cloud Computing ◽  
Public Opinion ◽  
Computer Technology ◽  
Information Dissemination ◽  
Transmission Mechanism ◽  
Practical Significance ◽  
Propagation Mechanism ◽  
Cross Media ◽  
User Density ◽  
Network Public Opinion

As an important expression of social public opinion, network public opinion develops rapidly with the popularization of the internet and then affects the real society. Therefore, the use of computer technology to study the network public opinion information transmission mechanism has strong practical significance. The purpose of this paper is to use cloud computing to realize the research of information dissemination mechanism in the context of cross-media public opinion network. Researched from three aspects of operator supervision, number of media, and user density, the hotspot propagation mechanism of Storm platform given in this paper can solve the efficiency problems of traditional algorithms while ensuring accuracy, improve efficiency, and lay the foundation for the research on the monitoring of Internet public opinion propagation.

scholarly journals Identifying Super-Spreader Nodes in Complex Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yu-Hsiang Fu ◽  
Chung-Yuan Huang ◽  
Chuen-Tsai Sun
Keyword(s):  
Infectious Diseases ◽  
Complex Networks ◽  
Complex Network ◽  
Information Dissemination ◽  
Local Features ◽  
Disease Detection ◽  
Network Nodes ◽  
Global Diversity ◽  
High Betweenness ◽  
Influential Individuals

Identifying the most influential individuals spreading information or infectious diseases can assist or hinder information dissemination, product exposure, and contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and highk-shell nodes have been identified as good initial spreaders, but efforts to use node diversity within network structures to measure spreading ability are few. Here we describe a two-step framework that combines global diversity and local features to identify the most influential network nodes. Results from susceptible-infected-recovered epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets.

Multidimensional influencing factors of public opinion information dissemination in social media: Evidence from Weibo dataset

2019 ◽  
Vol 33 (31) ◽  
pp. 1950375 ◽  
Author(s):  
Guanghui Wang ◽  
Yufei Wang ◽  
Kaidi Liu ◽  
Jimei Li
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Information Content ◽  
Influencing Factors ◽  
Network Structure ◽  
Information Dissemination ◽  
Influential Factors ◽  
Channel Network ◽  
The Public ◽  
Information Characteristics

The factors influencing the dissemination of public opinion on social media, the main carrier of public opinion, are diverse, complex and changeable. Existing studies of influential factors of public opinion dissemination focus on the information itself and information sources in the dissemination process, failing to consider the comprehensive influence of multidimensional factors, such as information content, sources and channels. This study takes the identification of multidimensional influential factors of social media information dissemination as the research object and comprehensively sorts out the influencing factors of public opinion. To improve the scientific basis and accuracy of the research, multidimensional factors, including information characteristics, dissemination network structure and user-level attributes, are selected to analyze the effect of influential factors in different dimensions on the dissemination of social media public opinion information using econometric models. Three main conclusions of this paper are as follows: (1) The traditional information characteristics (information content) and information source attributes (user-level factor) are not the only key factors affecting information dissemination, while the information channel (network structure) is worth more consideration. (2) Netizens tend to pay more attention to the psychological and emotional attributes of information when forwarding public opinions. The communication mode in which offline social elites enlighten the public no longer exists; whether a user is a network celebrity or lives in the central area no longer significantly affects public opinion dissemination. (3) The higher the total amount of information users release, the more the information would interfere with the public opinion. This is mainly because users with a higher level of activity may release more invalid information about advertising that has nothing to do with public opinion events.

scholarly journals Research on stock similarity and community division based on user attention sequence

2018 ◽  
Vol 189 ◽  
pp. 10022
Author(s):  
Gaowei Zhang ◽  
Lingyu Xu ◽  
Lei Wang
Keyword(s):  
Public Opinion ◽  
Complex Network ◽  
Result Show ◽  
User Groups ◽  
User Attention ◽  
Time Periods ◽  
New Perspective ◽  
Network Public Opinion

We conduct research from the perspective of user groups and analyze the differences in the users' attention and posting order in different time periods to vectorize stocks and build relationships from the generatedx vectors. This provides a new perspective for the complex network cconstruction and community division of network public opinion space. The experiment result show that we can get the community division consistent with reality using our model.

scholarly journals COVID-19 Information Dissemination Using the WeChat Communication Index: Retrospective Analysis Study (Preprint)

2021 ◽  
Author(s):  
Zina Fan ◽  
Wenqiang Yin ◽  
Han Zhang ◽  
Dandan Wang ◽  
Chengxin Fan ◽  
...  
Keyword(s):  
Public Opinion ◽  
Information Dissemination ◽  
Descriptive Analysis ◽  
Three Dimensions ◽  
The Public ◽  
Related Information ◽  
Information Governance ◽  
People’S Daily ◽  
People's Daily ◽  
Types Of Information

BACKGROUND The COVID-19 outbreak has tremendously impacted the world. The number of confirmed cases has continued to increase, causing damage to society and the economy worldwide. The public pays close attention to information on the pandemic and learns about the disease through various media outlets. The dissemination of comprehensive and accurate COVID-19 information that the public needs helps to educate people so they can take preventive measures. OBJECTIVE This study aimed to examine the dissemination of COVID-19 information by analyzing the information released by the official WeChat account of the <i>People’s Daily</i> during the pandemic. The most-read COVID-19 information in China was summarized, and the factors that influence information dissemination were studied to understand the characteristics that affect its dissemination. Moreover, this was conducted in order to identify how to effectively disseminate COVID-19 information and to provide suggestions on how to manage public opinion and information governance during a pandemic. METHODS This was a retrospective study based on a WeChat official account. We collected all COVID-19–related information, starting with the first report about COVID-19 from the <i>People’s Daily</i> and ending with the last piece of information about lifting the first-level emergency response in 34 Chinese provinces. A descriptive analysis was then conducted on this information, as well as on Qingbo Big Data’s dissemination index. Multiple linear regression was utilized to study the factors that affected information dissemination based on various characteristics and the dissemination index. RESULTS From January 19 to May 2, 2020, the <i>People’s Daily</i> released 1984 pieces of information; 1621 were related to COVID-19, which mainly included headline news items, items with emotional content, and issues related to the pandemic’s development. By analyzing the dissemination index, seven information dissemination peaks were discerned. Among the three dimensions of COVID-19 information—media salience, content, and format—eight factors affected the spread of COVID-19 information. CONCLUSIONS Different types of pandemic-related information have varying dissemination power. To effectively disseminate information and prevent the spread of COVID-19, we should identify the factors that affect this dissemination. We should then disseminate the types of information the public is most concerned about, use information to educate people to improve their health literacy, and improve public opinion and information governance.

A novel public opinion detection algorithm based on complex network

2018 ◽  
Vol 32 (s1) ◽  
pp. 125-134
Author(s):  
Meiling ZHOU ◽  
Gengxin SUN ◽  
Sheng BIN
Keyword(s):  
Public Opinion ◽  
Complex Network ◽  
Detection Algorithm
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