HISBmodel: A Rumor Diffusion Model Based on Human Individual and Social Behaviors in Online Social Networks

2018 ◽  
pp. 14-27 ◽  
Author(s):  
Adil Imad Eddine Hosni ◽  
Kan Li ◽  
Sadique Ahmed
Keyword(s):  
Social Networks ◽  
Diffusion Model ◽  
Online Social Networks ◽  
Social Behaviors ◽  
Model Based ◽  
Human Individual

Minimizing rumor influence in multiplex online social networks based on human individual and social behaviors

2020 ◽  
Vol 512 ◽  
pp. 1458-1480 ◽  
Author(s):  
Adil Imad Eddine Hosni ◽  
Kan Li ◽  
Sadique Ahmad
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Social Behaviors ◽  
Human Individual

Participation in Online Social Networks

2018 ◽  
Vol 8 (2) ◽  
pp. 36-55
Author(s):  
Giulio Angiani ◽  
Paolo Fornacciari ◽  
Eleonora Iotti ◽  
Monica Mordonini ◽  
Michele Tomaiuolo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Online Social Networks ◽  
Social Behaviors ◽  
Distributed Teams ◽  
Business Interests ◽  
Basic Features ◽  
Access To Knowledge ◽  
Organizational Costs

Why and how more and more people get involved and use social networking systems are critical topics in social network analysis (SNA). As a matter of fact, social networking systems bring online a growing number of acquaintances, for many different purposes. Both business interests and personal recreational goals are motivations for using online social networks (OSN) or other social networking systems. The participation in social networks is a phenomenon which has been studied with several theories, and SNA is useful for common business problems, e.g., launching distributed teams, retaining people with vital knowledge for the organization, improving access to knowledge and spreading ideas and innovation. Nevertheless, there are some difficulties, such as anti-social behaviors of participants, lack of incentives, organizational costs and risks. In this article, a survey of the basic features of SNA, participation theories and models are discussed, with emphasis on social capital, information spreading, motivations for participation, and anti-social behaviors of social network users.

Identification of Influential Online Social Network Users Based on Multi-Features

2016 ◽  
Vol 30 (06) ◽  
pp. 1659015 ◽  
Author(s):  
Qindong Sun ◽  
Nan Wang ◽  
Yadong Zhou ◽  
Zuomin Luo
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Online Social Network ◽  
Real Data ◽  
User Profiles ◽  
Sina Weibo ◽  
Pagerank Algorithm ◽  
Model Based ◽  
User Influence ◽  
Influential Users

The problem of discovering influential users is important to understand and analyze online social networks. The user profiles and interactions between users are significant features to evaluate the user influence. As these features are heterogeneous, it is challengeable to take all of them into a proper model for influence evaluation. In this paper, we propose a model based on personal user features and the adjacent factor to discover influential users in online social networks. Through taking the advantages of Bayesian network and chain principle of PageRank algorithm, the features of the user profiles and interactions are integratedly considered in our model. Based on real data from Sina Weibo data and multiple evaluation metrics of retweet count, tweet count, follower count, etc., the experimental results show that influential users identified by our model are more powerful than the ones identified by single indicator methods and PageRank-based methods.

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