Information Propagation in a Social Network: The Case of a Fish Schooling Algorithm

2015 ◽  
pp. 27-51 ◽  
Author(s):  
A. Brabazon ◽  
W. Cui ◽  
M. O’Neill
Keyword(s):  
Social Network ◽  
Information Propagation ◽  
Fish Schooling

Characteristic analysis of information propagation pattern in online social network

2013 ◽  
Vol 33 (1) ◽  
pp. 105-107 ◽  
Author(s):  
Jia HAN ◽  
Ruliang XIAO ◽  
Yao HU ◽  
Tao TANG ◽  
Lina FANG
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Information Propagation ◽  
Characteristic Analysis ◽  
Propagation Pattern

Viral information propagation in the Digg online social network

2014 ◽  
Vol 415 ◽  
pp. 87-94 ◽  
Author(s):  
Mark Freeman ◽  
James McVittie ◽  
Iryna Sivak ◽  
Jianhong Wu
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Information Propagation

Measure of the Content Creation Score on Social Network Using Sentiment Score and Passion Point

2020 ◽  
Author(s):  
Hien D. Nguyen ◽  
Tai Huynh ◽  
Son T. Luu ◽  
Suong N. Hoang ◽  
Vuong T. Pham ◽  
...  
Keyword(s):  
Social Network ◽  
Experimental Results ◽  
Information Propagation ◽  
Content Creation ◽  
Sentiment Score

Social network is one of efficient tools for spreading information. The evaluation of the content creation of a user is a useful feature to improve the ability of information propagation on social network. In this paper, the measures for evaluating the user’s content creation are proposed. They include the passion point of a user with a brand and the quality of the user’s posts. The passion point is computed based on the sentiment score of posting and the activity of the user. The quality of the user’s posts is computed through the analyzing of the post’s content. Those measures are combined to analyze the interesting of posts. The proposed method has been tested and get the positive experimental results.

scholarly journals A Measurement Model of Mutual Influence for Information Dissemination

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 725
Author(s):  
Liang Zhang ◽  
Yong Quan ◽  
Bin Zhou ◽  
Yan Jia ◽  
Liqun Gao
Keyword(s):  
Social Network ◽  
Information Dissemination ◽  
Mutual Influence ◽  
Synthetic Data ◽  
Measurement Model ◽  
Mobile Internet ◽  
Information Propagation ◽  
Information Cascade ◽  
Data Set ◽  
Access Information

The recent development of the mobile Internet and the rise of social media have significantly enriched the way people access information. Accurate modeling of the probability of information propagation between users is essential for studying information dissemination issues in social networks. As the dissemination of information is inseparable from the interactions between users, the probability of propagation can be characterized by such interactions. In general, there are differences in the dissemination modes of information that carry different topics in a real social network. Using these factors, we propose a method (TMIVM) to measure the mutual influence between users at the topic level. The method associates two vectorization parameters for each user—an influence vector and a susceptibility vector—where the dimensions of the vector represent different topic categories. The magnitude of the mutual influence between users on different topics can be obtained by the product of the corresponding elements of the vectors. Specifically, in this article, we fit a social network historical information cascade data through Survival Analysis to learn the parameters of the influence and susceptibility vectors. The experimental results on a synthetic data set and a real Microblog data set show that this method better measures the propagation probability and information cascade predictions compared to other methods.

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