Community Discovery and Behavior Prediction in Online Social Networks Employing Node Centrality

2020 ◽  
pp. 73-85
Author(s):  
Sanjeev Dhawan ◽  
Kulvinder Singh ◽  
Amit Batra
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Behavior Prediction ◽  
Community Discovery ◽  
Node Centrality ◽  
And Behavior

The Use of Online Social Networks and Its Influence on Job-Related Behavior

2019 ◽  
pp. 106-129
Author(s):  
Vera Silva Carlos ◽  
Ricardo Gouveia Rodrigues
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Online Survey ◽  
Positive Influence ◽  
Future Research ◽  
Work Related ◽  
Attitudes And Behaviors ◽  
Empirically Supported ◽  
And Behaviors ◽  
And Behavior

Web 2.0 technologies have progressively transformed social interactions among people. In addition, there is plenty of evidence of a positive influence of social relationships on work-related attitudes and behaviors. Within these frameworks, the purpose is to evaluate the effect of using online social networks on the workers' attitudes and behaviors, particularly in the context of higher education. The authors used an online survey to evaluate the attitudes and behavior of 157 faculty members. To assess the use of OSNs, they used a dichotomous variable. The t-student test and the PLS method were used to analyze the data. They conclude that the use of OSNs influences the workers' performance, but not job satisfaction, organizational commitment, or organizational citizenship behaviors (extra-role performance). The relationships they propose in what concerns the workers' attitudes are all empirically supported. Lastly, they describe the study limitations and we suggest some perspectives for future research.

Online Social Network Analysis

2019 ◽  
pp. 50-63
Author(s):  
Praveen Kumar Bhanodia ◽  
Aditya Khamparia ◽  
Babita Pandey ◽  
Shaligram Prajapat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Networks ◽  
Online Social Network ◽  
Structural Features ◽  
Network Properties ◽  
Prediction Systems ◽  
And Behavior

Expansion of online social networks is rapid and furious. Millions of users are appending to it and enriching the nature and behavior, and the information generated has various dimensional properties providing new opportunities and perspective for computation of network properties. The structure of social networks is comprised of nodes and edges whereas users are entities represented by node and relationships designated by edges. Processing of online social networks structural features yields fair knowledge which can be used in many of recommendation and prediction systems. This is referred to as social network analysis, and the features exploited usually are local and global both plays significant role in processing and computation. Local features include properties of nodes like degree of the node (in-degree, out-degree) while global feature process the path between nodes in the entire network. The chapter is an effort in the direction of online social network analysis that explores the basic methods that can be process and analyze the network with a suitable approach to yield knowledge.

scholarly journals A New Model for Rating Users’ Profiles in Online Social Networks

2017 ◽  
Vol 10 (2) ◽  
pp. 39 ◽  
Author(s):  
Mohammad Malli ◽  
Nadine Said ◽  
Ahmad Fadlallah
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Dimensional Space ◽  
New Model ◽  
Qualitative And Quantitative ◽  
Cartesian Space ◽  
Low Dimensional ◽  
Multiple Domains ◽  
And Behavior ◽  
Orthogonal Coordinates

Profiling users in Online Social Networks (OSNs) is of great benefit in multiple domains (e.g., marketing, sociology, and forensics). In this paper, we propose a new model for rating user’s profile (i.e., low, medium, high, and advanced) in an OSN community by embedding it into clusters located at predefined range of radius in a low-dimensional Cartesian space. The orthogonal coordinates of the profile are estimated using Principle Component Analysis (PCA) applied on a vector of metrics formulated as a set of attributes of interest (i.e., qualitative and quantitative) mined from the user’s profile to characterize his/her level of participation and behavior in the community. The experimentations are conducted on 3000 simulated profiles of three OSNs (Facebook, Twitter and Instagram) by embedding them in three cartesian spaces of three corresponding communities (Religion, Political and Lifestyle).  The results show that we are able to estimate accurately the profile rates by reducing the vector of metrics to a low-dimensional space whittle down to 3-D space.

scholarly journals Hot Topic Community Discovery on Cross Social Networks

2019 ◽  
Vol 11 (3) ◽  
pp. 60 ◽  
Author(s):  
Xuan Wang ◽  
Bofeng Zhang ◽  
Furong Chang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Latent Dirichlet Allocation ◽  
Rapid Development ◽  
Unified Model ◽  
Text Data ◽  
Community Discovery ◽  
Latent Topic ◽  
Discovery Method

The rapid development of online social networks has allowed users to obtain information, communicate with each other and express different opinions. Generally, in the same social network, users tend to be influenced by each other and have similar views. However, on another social network, users may have opposite views on the same event. Therefore, research undertaken on a single social network is unable to meet the needs of research on hot topic community discovery. “Cross social network” refers to multiple social networks. The integration of information from multiple social network platforms forms a new unified dataset. In the dataset, information from different platforms for the same event may contain similar or unique topics. This paper proposes a hot topic discovery method on cross social networks. Firstly, text data from different social networks are fused to build a unified model. Then, we obtain latent topic distributions from the unified model using the Labeled Biterm Latent Dirichlet Allocation (LB-LDA) model. Based on the distributions, similar topics are clustered to form several topic communities. Finally, we choose hot topic communities based on their scores. Experiment result on data from three social networks prove that our model is effective and has certain application value.

A Social Media Recommender System

2018 ◽  
Vol 9 (1) ◽  
pp. 36-50 ◽  
Author(s):  
Giancarlo Sperlì ◽  
Flora Amato ◽  
Fabio Mercorio ◽  
Mario Mezzanzanica ◽  
Vincenzo Moscato ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Recommender System ◽  
Online Social Networks ◽  
Online Social Network ◽  
Multimedia Contents ◽  
Big Data Applications ◽  
The Social ◽  
And Behavior ◽  
Content Information

Social media recommendation differs from traditional recommendation approaches as it needs considering not only the content information and users' similarities, but also users' social relationships and behavior within an online social network as well. In this article, a recommender system – designed for big data applications – is used for providing useful recommendations in online social networks. The proposed technique represents a collaborative and user-centered approach that exploits the interactions among users and generated multimedia contents in one or more social networks in a novel and effective way. The experiments performed on data collected from several online social networks show the feasibility of the approach towards the social media recommendation problem.

ANALYSIS OF THE CONDITIONS OF THE RELATION BETWEEN INTENTION AND BEHAVIOR IN ONLINE SOCIAL NETWORKS

2021 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Marcelo M. Brand�ão ◽  
Ariana M. De Souza ◽  
Amanda S. Z. Ferretti ◽  
Leonardo Q. Rocha
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
And Behavior

Privacy-preserved community discovery in online social networks

2019 ◽  
Vol 93 ◽  
pp. 1002-1009 ◽  
Author(s):  
Xu Zheng ◽  
Zhipeng Cai ◽  
Guangchun Luo ◽  
Ling Tian ◽  
Xiao Bai
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Community Discovery
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