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.

Learning to Correlate Accounts Across Online Social Networks: An Embedding-Based Approach

2020 ◽  
Vol 32 (3) ◽  
pp. 714-729
Author(s):  
Fan Zhou ◽  
Kunpeng Zhang ◽  
Shuying Xie ◽  
Xucheng Luo
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Dimensional Space ◽  
Synthetic Data ◽  
Language Models ◽  
Data Sets ◽  
User Behaviors ◽  
Latent Representations ◽  
Multiple Accounts ◽  
Cross Site

Cross-site account correlation correlates users who have multiple accounts but the same identity across online social networks (OSNs). Being able to identify cross-site users is important for a variety of applications in social networks, security, and electronic commerce, such as social link prediction and cross-domain recommendation. Because of either heterogeneous characteristics of platforms or some unobserved but intrinsic individual factors, the same individuals are likely to behave differently across OSNs, which accordingly causes many challenges for correlating accounts. Traditionally, account correlation is measured by analyzing user-generated content, such as writing style, rules of naming user accounts, or some existing metadata (e.g., account profile, account historical activities). Accounts can be correlated by de-anonymizing user behaviors, which is sometimes infeasible since such data are not often available. In this work, we propose a method, called ACCount eMbedding (ACCM), to go beyond text data and leverage semantics of network structures, a possibility that has not been well explored so far. ACCM aims to correlate accounts with high accuracy by exploiting the semantic information among accounts through random walks. It models and understands latent representations of accounts using an embedding framework similar to sequences of words in natural language models. It also learns a transformation matrix to project node representations into a common dimensional space for comparison. With evaluations on both real-world and synthetic data sets, we empirically demonstrate that ACCM provides performance improvement compared with several state-of-the-art baselines in correlating user accounts between OSNs.

A NOVEL MODEL FOR RUMOR SPREADING ON SOCIAL NETWORKS WITH CONSIDERING THE INFLUENCE OF DISSENTING OPINIONS

2018 ◽  
Vol 21 (06n07) ◽  
pp. 1850011 ◽  
Author(s):  
AMIRHOSEIN BODAGHI ◽  
SAMA GOLIAEI
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Realistic Model ◽  
The Novel ◽  
Human Beings ◽  
Rumor Spreading ◽  
New Model ◽  
Sociological Factors ◽  
Novel Model ◽  
New Hypothesis

Rumor spreading is a good sample of spreading in which human beings are the main players in the spreading process. Therefore, in order to have a more realistic model of rumor spreading on online social networks, the influence of psycho-sociological factors particularly those which affect users’ reactions toward rumor/anti-rumor should be considered. To this aim, we present a new model that considers the influence of dissenting opinions on those users who have already believed in rumor/anti-rumor but have not spread the rumor/anti-rumor yet. We hypothesize that influence is a motive for the believers to spread their beliefs in rumor/anti-rumor. We derive the stochastic equations of the new model and evaluate it by using two real datasets of rumor spreading on Twitter. The evaluation results support the new hypothesis and show that the novel model which is relied on the new hypothesis is able to better represent rumor spreading.

scholarly journals User Profile Preserving Social Network Embedding

2017 ◽  
Author(s):  
Daokun Zhang ◽  
Jie Yin ◽  
Xingquan Zhu ◽  
Chengqi Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Dimensional Space ◽  
User Profile ◽  
Network Embedding ◽  
Network Nodes ◽  
Profile Information ◽  
Low Dimensional ◽  
Performance Gains

This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPP-SNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPP-SNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.

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.

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

A Social Media Recommender System

2021 ◽  
pp. 541-556
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.

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