Cloud-Centric Assured Information Sharing for Social Networks

2016 ◽  
pp. 465-488
Keyword(s):  
Social Networks ◽  
Information Sharing

scholarly journals A machine-learning based approach to privacy-aware information-sharing in mobile social networks

2016 ◽  
Vol 25 ◽  
pp. 125-142 ◽  
Author(s):  
Igor Bilogrevic ◽  
Kévin Huguenin ◽  
Berker Agir ◽  
Murtuza Jadliwala ◽  
Maria Gazaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Information Sharing ◽  
Mobile Social Networks

Real Estate Information Sharing model for Social networks

2013 ◽  
Vol 7 (8) ◽  
pp. 788-793
Author(s):  
Lijun WAN ◽  
Guangxue WANG ◽  
Linsha HAN ◽  
chenguang ZHAO
Keyword(s):  
Social Networks ◽  
Real Estate ◽  
Information Sharing

scholarly journals Decentralized Private Information Sharing Protocol on Social Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shu-Chuan Chu ◽  
Lili Chen ◽  
Sachin Kumar ◽  
Saru Kumari ◽  
Joel J. P. C. Rodrigues ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Information Sharing ◽  
Mass Distribution ◽  
Private Information ◽  
Social Networking Sites ◽  
Control Protocol ◽  
Shared Information ◽  
Community Finding ◽  
Target Community

Social networks are becoming popular, with people sharing information with their friends on social networking sites. On many of these sites, shared information can be read by all of the friends; however, not all information is suitable for mass distribution and access. Although people can form communities on some sites, this feature is not yet available on all sites. Additionally, it is inconvenient to set receivers for a message when the target community is large. One characteristic of social networks is that people who know each other tend to form densely connected clusters, and connections between clusters are relatively rare. Based on this feature, community-finding algorithms have been proposed to detect communities on social networks. However, it is difficult to apply community-finding algorithms to distributed social networks. In this paper, we propose a distributed privacy control protocol for distributed social networks. By selecting only a small portion of people from a community, our protocol can transmit information to the target community.

Formation and Computation of Relationship in Complex Social Networks

2013 ◽  
Vol 427-429 ◽  
pp. 2687-2690
Author(s):  
Yu Ting Zhang ◽  
Gui Fa Teng
Keyword(s):  
Social Networks ◽  
Information Sharing ◽  
Social Activities ◽  
Computation Model ◽  
Model Based ◽  
Commercial Activities ◽  
Relationship Types ◽  
As Relationship ◽  
Real Complex

Social networks provide customs with a platform for interaction and information sharing. In real social activities, whether individuals or businesses, have to rely on some relations to live, work or engage in commercial activities. The formation of relationship between different actor clusters based on the same actor in real social networks is described in the paper. The relationships and their types as well as relationship attributes in real complex social networks are analyzed in details. An index calledQinshuduto represent the degree of closeness between two actors in real complex social networks is proposed and its computation model based on relationship types and attributes etc. are given. Case study shows thatQinshuduis a very reasonable and effective way for the use of complex social networks.

The Selective Communication of Political Information

2020 ◽  
pp. 194855062094236
Author(s):  
Pierce D. Ekstrom ◽  
Calvin K. Lai
Keyword(s):  
Social Networks ◽  
Information Sharing ◽  
Minimum Wage ◽  
Political Orientation ◽  
Political Information ◽  
Political Polarization ◽  
The Political ◽  
Negative Effects ◽  
Ideological Differences ◽  
Share Information

People seek out and interpret political information in self-serving ways. In four experiments, we show that people are similarly self-serving in the political information they share with others. Participants learned about positive and negative effects of increasing the minimum wage (in Studies 1–3) or of banning assault weapons (Study 4). They then indicated how likely they would be to mention each effect to close others. Participants were more inclined to share information that was consistent with their political orientation than information that was not. This effect persisted even when participants believed the information, suggesting that selective communication is not just a reflection of motivated skepticism. We also observed ideological differences. Liberals were most biased with their political opponents, whereas conservatives were most biased with their political allies. This biased information sharing could distort the flow of political information through social networks in ways that exacerbate political polarization.

scholarly journals Enhancing topology adaptation in information-sharing social networks

2012 ◽  
Vol 85 (4) ◽  
Author(s):  
Giulio Cimini ◽  
Duanbing Chen ◽  
Matúš Medo ◽  
Linyuan Lü ◽  
Yi-Cheng Zhang ◽  
...  
Keyword(s):  
Social Networks ◽  
Information Sharing ◽  
Topology Adaptation

Effects of customers' psychological characteristics on their engagement behavior in company social networks

2016 ◽  
Vol 44 (10) ◽  
pp. 1661-1670 ◽  
Author(s):  
Lingyun Guo ◽  
Mingli Zhang ◽  
Yu Wang
Keyword(s):  
Social Networks ◽  
Information Sharing ◽  
Word Of Mouth ◽  
Need For Cognition ◽  
Psychological Ownership ◽  
Psychological Characteristics ◽  
Customer Engagement ◽  
Equation Modeling ◽  
Engagement Behavior ◽  
Value Cocreation

Drawing upon customer engagement and value cocreation theories, in the context of company social networks, we examined the influence mechanism of the 3 important psychological characteristics of need for cognition, self-congruity, and psychological ownership, on customer engagement behavior in terms of human interactivity, information sharing, and word-of-mouth referral. We also explored whether or not these 3 behaviors then influenced value cocreation and stickiness. Data from WeChat official account users were analyzed using SmartPLS 2.0 and structural equation modeling. Results showed that self-congruity and psychological ownership significantly influenced the 3 dimensions of customer engagement behavior, which further influenced value cocreation and stickiness. The effects of need for cognition on human interactivity and information sharing were also significant, but the influence of need for cognition on word-of-mouth referral was nonsignificant.

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