scholarly journals Popularity Evaluation Model for Microbloggers Online Social Network

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Xia Zhang ◽  
Zhengyou Xia ◽  
Zhan Bu
Keyword(s):  
Electromagnetic Field ◽  
Social Network ◽  
Information Dissemination ◽  
User Behavior ◽  
Evaluation Model ◽  
Online Social Network ◽  
Research Field ◽  
User Behavior Analysis ◽  
Measure Methods ◽  
Commercial Advertising

Recently, microblogging is widely studied by the researchers in the domain of the online social network (OSN). How to evaluate the popularities of microblogging users is an important research field, which can be applied to commercial advertising, user behavior analysis and information dissemination, and so forth. Previous studies on the evaluation methods cannot effectively solve and accurately evaluate the popularities of the microbloggers. In this paper, we proposed an electromagnetic field theory based model to analyze the popularities of microbloggers. The concept of the source in microblogging field is first put forward, which is based on the concept of source in the electromagnetic field; then, one’s microblogging flux is calculated according to his/her behaviors (send or receive feedbacks) on the microblogging platform; finally, we used three methods to calculate one’s microblogging flux density, which can represent one’s popularity on the microblogging platform. In the experimental work, we evaluated our model using real microblogging data and selected the best one from the three popularity measure methods. We also compared our model with the classic PageRank algorithm; and the results show that our model is more effective and accurate to evaluate the popularities of the microbloggers.

scholarly journals Trust-Based Access Control Model from Sociological Approach in Dynamic Online Social Network Environment

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Seungsoo Baek ◽  
Seungjoo Kim
Keyword(s):  
Social Network ◽  
Access Control ◽  
User Behavior ◽  
Online Social Network ◽  
Sociological Approach ◽  
Access Control Models ◽  
Control Rules ◽  
Access Control Rules ◽  
Dynamic Trust ◽  
Privacy Breaches

There has been an explosive increase in the population of the OSN (online social network) in recent years. The OSN provides users with many opportunities to communicate among friends and family. Further, it facilitates developing new relationships with previously unknown people having similar beliefs or interests. However, the OSN can expose users to adverse effects such as privacy breaches, the disclosing of uncontrolled material, and the disseminating of false information. Traditional access control models such as MAC, DAC, and RBAC are applied to the OSN to address these problems. However, these models are not suitable for the dynamic OSN environment because user behavior in the OSN is unpredictable and static access control imposes a burden on the users to change the access control rules individually. We propose a dynamic trust-based access control for the OSN to address the problems of the traditional static access control. Moreover, we provide novel criteria to evaluate trust factors such as sociological approach and evaluate a method to calculate the dynamic trust values. The proposed method can monitor negative behavior and modify access permission levels dynamically to prevent the indiscriminate disclosure of information.

scholarly journals A Community Finding Method for Weighted Dynamic Online Social Network Based on User Behavior

2015 ◽  
Vol 11 (6) ◽  
pp. 306160 ◽  
Author(s):  
Dongming Chen ◽  
Yanlin Dong ◽  
Xinyu Huang ◽  
Haiyan Chen ◽  
Dongqi Wang
Keyword(s):  
Social Network ◽  
User Behavior ◽  
Online Social Network ◽  
Community Finding ◽  
Finding Method

Application of clustering algorithm in social network user scenario prediction

2020 ◽  
Vol 39 (4) ◽  
pp. 4971-4979
Author(s):  
Xiaoxian Wen ◽  
Yunhui Ma ◽  
Jiaxin Fu ◽  
Jing Li
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Behavior Analysis ◽  
Clustering Algorithm ◽  
User Behavior ◽  
Characteristic Analysis ◽  
User Behavior Analysis ◽  
Multimedia Social Networks ◽  
Scenario Prediction ◽  
User Scenario

In order to improve the ability of social network user behavior analysis and scenario pattern prediction, optimize social network construction, combine data mining and behavior analysis methods to perform social network user characteristic analysis and user scenario pattern optimization mining, and discover social network user behavior characteristics. Design multimedia content recommendation algorithms in multimedia social networks based on user behavior patterns. The current existing recommendation systems do not know how much the user likes the currently viewed content before the user scores the content or performs other operations, and the user’s preference may change at any time according to the user’s environment and the user’s identity, Usually in multimedia social networks, users have their own grading habits, or users’ ratings may be casual. Cluster-based algorithm, as an application of cluster analysis, based on clustering, the algorithm can predict the next position of the user. Because the algorithm has a “cold start”, it is suitable for new users without trajectories. You can also make predictions. In addition, the algorithm also considers the user’s feedback information, and constructs a scoring system, which can optimize the results of location prediction through iteration. The simulation results show that the accuracy of social network user scenario prediction using this method is higher, the accuracy of feature registration of social network user scenario mode is improved, and the real-time performance of algorithm processing is better.

Information Propagation and Public Opinion Evolution Model Based on Artificial Neural Network in Online Social Network

2019 ◽  
Vol 63 (11) ◽  
pp. 1689-1703 ◽  
Author(s):  
Xiaoyang Liu ◽  
Daobing He
Keyword(s):  
Neural Network ◽  
Public Opinion ◽  
Social Network ◽  
Information Dissemination ◽  
Online Social Network ◽  
Information Propagation ◽  
Propagation Algorithm ◽  
Information Interaction ◽  
Opinion Evolution ◽  
Network Public Opinion

Abstract This paper proposes a new information dissemination and opinion evolution IPNN (Information Propagation Neural Network) model based on artificial neural network. The feedforward network, feedback network and dynamic evolution algorithms are designed and implemented. Firstly, according to the ‘six degrees separation’ theory of information dissemination, a seven-layer neural network underlying framework with input layer, propagation layer and termination layer is constructed; secondly, the information sharing and information interaction evolution process between nodes are described by using the event information forward propagation algorithm, opinion difference reverse propagation algorithm; finally, the external factors of online social network information dissemination is considered, the impact of external behavior patterns is measured by media public opinion guidance and network structure dynamic update operations. Simulation results show that the proposed new mathematical model reveals the relationship between the state of micro-network nodes and the evolution of macro-network public opinion. It accurately depicts the internal information interaction mechanism and diffusion mechanism in online social network. Furthermore, it reveals the process of network public opinion formation and the nature of public opinion explosion in online social network. It provides a new scientific method and research approach for the study of social network public opinion evolution.

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