Social influence‐based privacy inference attacks in online social networks

2021 ◽  
Author(s):  
Yuzi Yi ◽  
Jingsha He ◽  
Nafei Zhu ◽  
Xiangjun Ma
Keyword(s):  
Social Networks ◽  
Social Influence ◽  
Online Social Networks ◽  
Inference Attacks

Temporal Topic-Based Multi-Dimensional Social Influence Evaluation in Online Social Networks

2017 ◽  
Vol 95 (3) ◽  
pp. 2143-2171 ◽  
Author(s):  
Feng Wang ◽  
Jianbin Li ◽  
Wenjun Jiang ◽  
Guojun Wang
Keyword(s):  
Social Networks ◽  
Social Influence ◽  
Online Social Networks

Understanding Weight Change Behaviors through Online Social Networks

2011 ◽  
Vol 2 (3) ◽  
pp. 46-69 ◽  
Author(s):  
Xiaoxiao Ma ◽  
Guanling Chen ◽  
Juntao Xiao
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Influence ◽  
Empirical Analysis ◽  
Real World ◽  
Weight Change ◽  
Online Social Networks ◽  
Community Support ◽  
Propagation Distance ◽  
Weight Changes

Online Social Networks (OSNs) provide a good way to make connections with people with similar interests and goals. In particular, health-centered OSNs are emerging to provide knowledge and support for those interested in managing their own health. This paper provides an empirical analysis of a health OSN, which allows its users to record their foods and exercises, track their diet progress toward weight-change goals, and socialize and group with each other for community support. Based on about five month data collected from more than 107,000 users, the authors studied their weigh-in behaviors and tracked their weight-change progress. The authors found that the users’ weight changes correlated positively with the number of weigh-ins, the number of their friends, and their friends’ weight-change performance. The authors also show that the users’ weight changes have rippling effects in the OSN due to social influence. The strength of such online influence and its propagation distance appear to be greater than those in a real-world social network.

scholarly journals Efficient Estimation of Network Games of Incomplete Information: Application to Large Online Social Networks

2021 ◽  
Author(s):  
Xi Chen ◽  
Ralf van der Lans ◽  
Michael Trusov
Keyword(s):  
Social Networks ◽  
Social Influence ◽  
Incomplete Information ◽  
Community Detection ◽  
Online Social Networks ◽  
Choice Model ◽  
Real Life ◽  
Efficient Estimation ◽  
Data Set ◽  
Detection Algorithms

This paper presents a structural discrete choice model with social influence for large-scale social networks. The model is based on an incomplete information game and permits individual-specific parameters of consumers. It is challenging to apply this type of models to real-life scenarios for two reasons: (1) The computation of the Bayesian–Nash equilibrium is highly demanding; and (2) the identification of social influence requires the use of excluded variables that are oftentimes unavailable. To address these challenges, we derive the unique equilibrium conditions of the game, which allow us to employ a stochastic Bayesian estimation procedure that is scalable to large social networks. To facilitate the identification, we utilize community-detection algorithms to divide the network into different groups that, in turn, can be used to construct excluded variables. We validate the proposed structural model with the login decisions of more than 25,000 users of an online social game. Importantly, this data set also contains promotions that were exogenously determined and targeted to only a subgroup of consumers. This information allows us to perform exogeneity tests to validate our identification strategy using community-detection algorithms. Finally, we demonstrate the managerial usefulness of the proposed methodology for improving the strategies of targeting influential consumers in large social networks. This paper was accepted by Matthew Shum, marketing.

scholarly journals Cross-site Prediction on Social Influence for Cold-start Users in Online Social Networks

2021 ◽  
Vol 15 (2) ◽  
pp. 1-23
Author(s):  
Qingyuan Gong ◽  
Yang Chen ◽  
Xinlei He ◽  
Yu Xiao ◽  
Pan Hui ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Influence ◽  
Online Social Networks ◽  
Cold Start ◽  
Learning Technologies ◽  
Viral Marketing ◽  
Supervised Machine Learning ◽  
Activity Data ◽  
Public Data ◽  
Influential Users

Online social networks (OSNs) have become a commodity in our daily life. As an important concept in sociology and viral marketing, the study of social influence has received a lot of attentions in academia. Most of the existing proposals work well on dominant OSNs, such as Twitter, since these sites are mature and many users have generated a large amount of data for the calculation of social influence. Unfortunately, cold-start users on emerging OSNs generate much less activity data, which makes it challenging to identify potential influential users among them. In this work, we propose a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user’s information on dominant OSNs. A supervised machine learning-based approach is adopted, transferring the knowledge of both the descriptive information and dynamic activities on dominant OSNs. Descriptive features are extracted from the public data on a user’s homepage. In particular, to extract useful information from the fine-grained dynamic activities that cannot be represented by the statistical indices, we use deep learning technologies to deal with the sequential activity data. Using the real data of millions of users collected from Twitter (a dominant OSN) and Medium (an emerging OSN), we evaluate the performance of our proposed framework to predict prospective influential users. Our system achieves a high prediction performance based on different social influence definitions.

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