scholarly journals User Profiles Matching for Different Social Networks Based on Faces Identification

2019 ◽  
pp. 551-562
Author(s):  
Timur Sokhin ◽  
Nikolay Butakov ◽  
Denis Nasonov
Keyword(s):  
Social Networks ◽  
User Profiles

Using media related user profiles to personalize multimedia access over social networks

2011 ◽  
Author(s):  
Lemonia Argyriou ◽  
Charalampos Z. Patrikakis ◽  
Stuart CM Porter ◽  
NIkolaos Papaoulakis ◽  
Christina Androulaki
Keyword(s):  
Social Networks ◽  
User Profiles ◽  
Multimedia Access

scholarly journals Application of Machine Learning Methods in the Task of Identifying User Accounts in Two Social Networks

2019 ◽  
pp. 29-43
Author(s):  
Anastasiya A. Korepanova ◽  
◽  
Valerii D. Oliseenko ◽  
Maxim V. Abramov ◽  
Alexander L. Tulupyev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Information System ◽  
New Combination ◽  
Practical Significance ◽  
User Profiles ◽  
Learning Models ◽  
Machine Learning Methods ◽  
The Social ◽  
Machine Learning Models

The article describes the approach to solving the problem of comparing user profiles of different social networks and identifying those that belong to one person. An appropriate method is proposed based on a comparison of the social environment and the values of account profile attributes in two different social networks. The results of applying various machine learning models to solving this problem are compared. The novelty of the approach lies in the proposed new combination of various methods and application to new social networks. The practical significance of the study is to automate the process of determining the ownership of profiles in various social networks to one user. These results can be applied in the task of constructing a meta-profile of a user of an information system for the subsequent construction of a profile of his vulnerabilities, as well as in other studies devoted to social networks.

scholarly journals Enhanced Techniques for Filtering of Wall Messages over Online Social Networks (OSN) User Profiles

2015 ◽  
Vol 5 (4) ◽  
pp. 47-61 ◽  
Author(s):  
Nikhil Sanyog Choudhary ◽  
◽  
Himanshu Yadav ◽  
Anurag Jain
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
User Profiles

scholarly journals Adaptive method of similarity detection of user profiles on online social networks

2019 ◽  
Vol 68 (2) ◽  
pp. 43-57
Author(s):  
Michał Zabielski ◽  
Zbigniew Tarapata ◽  
Rafał Kasprzyk
Keyword(s):  
Social Networks ◽  
Network Theory ◽  
Online Social Networks ◽  
User Profile ◽  
Adaptive Method ◽  
User Profiles ◽  
Simulation Environment ◽  
Similarity Detection ◽  
The Web

The paper presents a method, based on graph and network theory, which allows to detect cloned user profiles on Online Social Networks. Moreover, an idea of similarity containers, which gives an opportunity to incorporate importance and context of data into a model, was introduced. The presented solutions were adapted to the idea of simulation environment, which will allow to detect a profile cloning process before that activity will be completely performed by an attacker. Keywords: Online Social Networks, user profile cloning, violation of privacy on the web.

scholarly journals A Profile Analysis of User Interaction in Social Media Using Deep Learning

2021 ◽  
Vol 38 (1) ◽  
pp. 1-11
Author(s):  
Hafzullah İş ◽  
Taner Tuncer
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Profile Analysis ◽  
User Interaction ◽  
Network Models ◽  
User Profiles ◽  
Success Rates ◽  
Neural Network Models ◽  
Data Set

It is highly important to detect malicious account interaction in social networks with regard to political, social and economic aspects. This paper analyzed the profile structure of social media users using their data interactions. A total of 10 parameters including diameter, density, reciprocity, centrality and modularity were used to comprehensively characterize the interactions of Twitter users. Moreover, a new data set was formed by visualizing the data obtained with these parameters. User profiles were classified using Convolutional Neural Network models with deep learning. Users were divided into active, passive and malicious classes. Success rates for the algorithms used in the classification were estimated based on the hyper parameters and application platforms. The best model had a success rate of 98.67%. The methodology demonstrated that Twitter user profiles can be classified successfully through user interaction-based parameters. It is expected that this paper will contribute to published literature in terms of behavioral analysis and the determination of malicious accounts in social networks.

Analysis of User Profiles in Social Networks

2014 ◽  
pp. 70-76 ◽  
Author(s):  
Alexander A. Chumak ◽  
Sergei S. Ukustov ◽  
Alla G. Kravets
Keyword(s):  
Social Networks ◽  
User Profiles

ANALYSIS OF USER PROFILES IN SOCIAL NETWORKS TO SEARCH FOR PROMISING ENTRANTS

2017 ◽  
Author(s):  
Artem Feshchenko ◽  
Viacheslav Goiko ◽  
Galina Mozhaeva ◽  
Konstantin Shilyaev ◽  
Andrey Stepanenko
Keyword(s):  
Social Networks ◽  
User Profiles

Linking user profiles in social networks: a comparative review

2017 ◽  
Vol 2 (4) ◽  
pp. 333 ◽  
Author(s):  
Hussein Hazimeh ◽  
Elena Mugellini ◽  
Omar Abou Khaled ◽  
Philippe Cudré Mauroux
Keyword(s):  
Social Networks ◽  
User Profiles ◽  
Comparative Review
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