scholarly journals DeepOSN: Bringing deep learning as malicious detection scheme in online social network

2020 ◽  
Vol 9 (1) ◽  
pp. 146
Author(s):  
Putra Wanda ◽  
Marselina Endah Hiswati ◽  
Huang J. Jie
Keyword(s):  
Social Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Online Social Networks ◽  
Link Prediction ◽  
Online Social Network ◽  
Detection Scheme ◽  
Malicious Activity ◽  
Manual Analysis ◽  
Learning Techniques

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

scholarly journals Link Prediction in Facebook using Web Scrapping and Deep Learning Techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 194-197
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Link Prediction ◽  
Machine Learning Techniques ◽  
Feature Engineering ◽  
Internet Technologies ◽  
Learning Techniques ◽  
The Social ◽  
Feature Based ◽  
Day By Day

As Internet technologies develop continuously social networks are getting more popular day by day. People are connected with each other via virtual applications. Using the Link Prediction in social networks more people get connected, may be they are friends, may be work together at the same workplace and may be their education are. Machine learning techniques are used to analyze the link between the nodes of the network and also create a better link prediction model through deep learning. The objective of this research is to measure the performance using the different techniques to predict link between the social networks. Using deep learning, feature engineering can be reduced for link prediction. In this research, the feature based learning is used to predict the link for better performance. Dataset is obtained by scraping the profile of Facebook users and they are used along with the random forest and graph convolution neural network to measure the performance of link prediction in social networks.

scholarly journals Abnormal User Detection of Malicious Accounts in Online Social Networks using Cookie Based Cross Verification

2019 ◽  
Vol 8 (9S4) ◽  
pp. 202-205
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Learning Algorithm ◽  
Online Social Network ◽  
Research Work ◽  
User Profile ◽  
Poor Quality ◽  
Malicious Activity ◽  
Online Social Media ◽  
Work Done

Malicious account detecting is a serious problem on the Internet today. Online social media services like Facebook, LinkedIn, and Instagram, these services include good quality service like opinions, comments as well as poor quality services like rumors, spam, and other malicious activity. In this paper, we review the existing research work done on Facebook, Instagram and LinkedIn, study the techniques used to identify and analyze the poor quality content on Facebook, and other social networks, and we proposed a combined technique like dynamic user profile verification and cookie-based cross-verification to detect malicious activity in an online social network by using random forest machine learning algorithm We also attempt to understand the limitations posed by Facebook in terms of availability of data for collection, and analysis, and try to understand if existing techniques can be used to identify and study poor quality content on Facebook and other social networks.

scholarly journals How migrants manifest their transnational identity through online social networks: comparative findings from a case of Koreans in Germany

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Sunyoung Park ◽  
Lasse Gerrits
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Online Social Network ◽  
Point Of View ◽  
Comparative Case Study ◽  
Transnational Identity ◽  
Transnational Identities ◽  
Web 3.0 ◽  
Situational Contexts

AbstractAlthough migration has long been an imperative topic in social sciences, there are still needs of study on migrants’ unique and dynamic transnational identity, which heavily influences the social integration in the host society. In Online Social Network (OSN), where the contemporary migrants actively communicate and share their stories the most, different challenges against migrants’ belonging and identity and how they cope or reconcile may evidently exist. This paper aims to scrutinise how migrants are manifesting their belonging and identity via different technological types of online social networks, to understand the relations between online social networks and migrants’ multi-faceted transnational identity. The research introduces a comparative case study on an online social movement led by Koreans in Germany via their online communities, triggered by a German TV advertisement considered as stereotyping East Asians given by white supremacy’s point of view. Starting with virtual ethnography on three OSNs representing each of internet generations (Web 1.0 ~ Web 3.0), two-step Qualitative Data Analysis is carried out to examine how Korean migrants manifest their belonging and identity via their views on “who we are” and “who are others”. The analysis reveals how Korean migrants’ transnational identities differ by their expectation on the audience and the members in each online social network, which indicates that the distinctive features of the online platform may encourage or discourage them in shaping transnational identity as a group identity. The paper concludes with the two main emphases: first, current OSNs comprising different generational technologies play a significant role in understanding the migrants’ dynamic social values, and particularly, transnational identities. Second, the dynamics of migrants’ transnational identity engages diverse social and situational contexts. (keywords: transnational identity, migrants’ online social networks, stereotyping migrants, technological evolution of online social network).

Reducing Cumulative Errors of Incremental CP Decomposition in Dynamic Online Social Networks

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Jingjing Wang ◽  
Wenjun Jiang ◽  
Kenli Li ◽  
Keqin Li
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Prediction Error ◽  
High Efficiency ◽  
Optimization Problems ◽  
Online Social Network ◽  
Reconstruction Error ◽  
Incremental Updating ◽  
Cp Decomposition ◽  
Error Accumulation

CANDECOMP/PARAFAC (CP) decomposition is widely used in various online social network (OSN) applications. However, it is inefficient when dealing with massive and incremental data. Some incremental CP decomposition (ICP) methods have been proposed to improve the efficiency and process evolving data, by updating decomposition results according to the newly added data. The ICP methods are efficient, but inaccurate because of serious error accumulation caused by approximation in the incremental updating. To promote the wide use of ICP, we strive to reduce its cumulative errors while keeping high efficiency. We first differentiate all possible errors in ICP into two types: the cumulative reconstruction error and the prediction error. Next, we formulate two optimization problems for reducing the two errors. Then, we propose several restarting strategies to address the two problems. Finally, we test the effectiveness in three typical dynamic OSN applications. To the best of our knowledge, this is the first work on reducing the cumulative errors of the ICP methods in dynamic OSNs.

Quantifying Virality of Information in Online Social Networks

2012 ◽  
Vol 4 (1) ◽  
pp. 32-45 ◽  
Author(s):  
Abhishek Vaish ◽  
Rajiv Krishna G. ◽  
Akshay Saxena ◽  
Dharmaprakash M. ◽  
Utkarsh Goel
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Online Social Network ◽  
Critical Factors ◽  
Asset Valuation ◽  
Valuation Technique ◽  
Information Item ◽  
Viral Nature ◽  
Over Time

The aim of this research is to propose a model through which the viral nature of an information item in an online social network can be quantified. Further, the authors propose an alternate technique for information asset valuation by accommodating virality in it which not only complements the existing valuation system, but also improves the accuracy of the results. They use a popularly available YouTube dataset to collect attributes and measure critical factors such as share-count, appreciation, user rating, controversiality, and comment rate. These variables are used with a proposed formula to obtain viral index of each video on a given date. The authors then identify a conventional and a hybrid asset valuation technique to demonstrate how virality can fit in to provide accurate results.The research demonstrates the dependency of virality on critical social network factors. With the help of a second dataset acquired, the authors determine the pattern virality of an information item takes over time.

scholarly journals Defining Inspection Techniques for Detecting Privacy Problems in Online Social Networks

2019 ◽  
Vol 10 ◽  
pp. 35
Author(s):  
Andrey  Rodrigues ◽  
Natasha  M. C. Valentim ◽  
Eduardo  Feitosa
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Empirical Study ◽  
Qualitative Analysis ◽  
Online Social Networks ◽  
Online Social Network ◽  
Effective Alternative ◽  
Daily Lives ◽  
Study Participants ◽  
Inspection Techniques

In the last few years, Online Social Networks (OSN) have experienced growth in the number of users, becoming an increasingly embedded part of people’s daily lives. Privacy expectations of OSNs are higher as more members start realizing potential privacy problems they face by interacting with these systems. Inspection methods can be an effective alternative for addressing privacy problems because they detect possible defects that could be causing the system to behave in an undesirable way. Therefore, we proposed a set of privacy inspection techniques called PIT-OSN (Privacy Inspection Techniques for Online Social Network). This paper presents the description and evolution of PIT-OSN through the results of a preliminary empirical study. We discuss the quantitative and qualitative results and their impact on improving the techniques. Results indicate that our techniques assist non-expert inspectors uncover privacy problems effectively, and are considered easy to use and useful by the study participants. Finally, the qualitative analysis helped us improve some technique steps that might be unclear.

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