scholarly journals Review and Analysis on Filtering of Unwanted Multimedia Messages from Online Social Network User Walls

2021 ◽  
Vol 9 (12) ◽  
pp. 1815-1819
Author(s):  
Martand Ratnam
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Online Social Network ◽  
Information Filtering ◽  
Human Mind ◽  
Sensitive Data ◽  
Public Areas ◽  
Shared Information ◽  
Black List ◽  
Types Of Information

Abstract: When it comes to sharing and exchanging various types of information, online social networks (OSNs) have become an increasingly popular and interactive medium in today's world. People who are connected to blogs and social networks see all of the publicly shared information, and it has a profound effect on the human mind. Messages or comments posted on a wall, a public or private area, may include unnecessary information or sensitive data. Thus, online social networks can benefit from information filtering, which can be used to help users organise messages written in public areas by removing unnecessary words. An information filtering system proposed in this paper may allow OSN users to control the posting and commenting on their walls directly. Every time a user posts a message, the message is intercepted by the filtered wall, which then applies Filtering and Black List Rules to it. The message will appear on the user's wall if it is not filtered or blacklisted. Keywords: Content Based Message Filtering, Demographic Filtering, Collaborative Filtering.

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.

scholarly journals Privacy and Security Issues in Online Social Networks

2018 ◽  
Vol 10 (12) ◽  
pp. 114 ◽  
Author(s):  
Shaukat Ali ◽  
Naveed Islam ◽  
Azhar Rauf ◽  
Ikram Din ◽  
Mohsen Guizani ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Virtual Communities ◽  
Service Providers ◽  
Security And Privacy ◽  
Security Issue ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Security Issues ◽  
Social Sphere

The advent of online social networks (OSN) has transformed a common passive reader into a content contributor. It has allowed users to share information and exchange opinions, and also express themselves in online virtual communities to interact with other users of similar interests. However, OSN have turned the social sphere of users into the commercial sphere. This should create a privacy and security issue for OSN users. OSN service providers collect the private and sensitive data of their customers that can be misused by data collectors, third parties, or by unauthorized users. In this paper, common security and privacy issues are explained along with recommendations to OSN users to protect themselves from these issues whenever they use social media.

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.

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.

Privacy Information Leakage Prevention in Cognitive Social Mining Applications

2019 ◽  
pp. 188-212
Author(s):  
Suriya Murugan ◽  
Anandakumar H.
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Private Information ◽  
Online Social Networks ◽  
Information Leakage ◽  
User Profile ◽  
User Privacy ◽  
Sensitive Data ◽  
Data Set ◽  
Secure Information

Online social networks, such as Facebook are increasingly used by many users and these networks allow people to publish and share their data to their friends. The problem is user privacy information can be inferred via social relations. This chapter makes a study and performs research on managing those confidential information leakages which is a challenging issue in social networks. It is possible to use learning methods on user released data to predict private information. Since the main goal is to distribute social network data while preventing sensitive data disclosure, it can be achieved through sanitization techniques. Then the effectiveness of those techniques is explored, and the methods of collective inference are used to discover sensitive attributes of the user profile data set. Hence, sanitization methods can be used efficiently to decrease the accuracy of both local and relational classifiers and allow secure information sharing by maintaining user privacy.

Social Identities, Group Formation, and the Analysis of Online Communities

2010 ◽  
pp. 1346-1361 ◽  
Author(s):  
Jillianne R. Code ◽  
Nicholas E. Zaparyniuk
Keyword(s):  
Social Networks ◽  
Social Identity ◽  
Online Communities ◽  
Collective Identity ◽  
Online Social Networks ◽  
Online Social Network ◽  
Group Formation ◽  
Social Identities ◽  
New Members ◽  
The Individual

Central to research in social psychology is the means in which communities form, attract new members, and develop over time. Research has found that the relative anonymity of Internet communication encourages self-expression and facilitates the formation of relationships based on shared values and beliefs. Self-expression in online social networks enables identity experimentation and development. As identities are fluid, situationally contingent, and are the perpetual subject and object of negotiation within the individual, the presented and perceived identity of the individual may not match reality. In this chapter, the authors consider the psychological challenges unique to understanding the dynamics of social identity formation and strategic interaction in online social networks. The psychological development of social identities in online social network interaction is discussed, highlighting how collective identity and self-categorization associates social identity to online group formation. The overall aim of this chapter is to explore how social identity affects the formation and development of online communities, how to analyze the development of these communities, and the implications such social networks have within education.

Building Online Social Networks to Engage Female Students in Information Systems

2015 ◽  
Vol 10 (4) ◽  
pp. 33-51
Author(s):  
Jaymeen R. Shah ◽  
Hsun-Ming Lee
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Systems ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Role Models ◽  
Online Social Network ◽  
Research Process ◽  
Female Students ◽  
The Social

During the next decade, enrollment growth in Information Systems (IS) related majors is unlikely to meet the predicted demand for qualified IS graduates. Gender imbalance in the IS related program makes the situation worse as enrollment and retention of women in the IS major has been proportionately low compared to male. In recent years, majority of high school and college students have integrated social networking sites in their daily life and habitually use these sites. Providing female students access to role models via an online social network may enhance their motivation to continue as an IS major and pursue a career in IS field. For this study, the authors follow the action research process – exploration of information systems development. In particular, a Facebook application was developed to build the social network connecting role models and students. Using the application, a basic framework is tested based on the gender of participants. The results suggest that it is necessary to have adequate number of role models accessible to students as female role-models tend to select fewer students to develop relationships with a preference for female students. Female students likely prefer composite role models from a variety of sources. This pilot study yields valuable lessons to provide informal learning fostered by role modeling via online social networks. The Facebook application may be further expanded to enhance female students' interests in IS related careers.

Social Identities, Group Formation, and the Analysis of Online Communities

2010 ◽  
pp. 86-101 ◽  
Author(s):  
Jillianne R. Code ◽  
Nicholas E. Zaparyniuk
Keyword(s):  
Social Networks ◽  
Social Identity ◽  
Online Communities ◽  
Collective Identity ◽  
Online Social Networks ◽  
Online Social Network ◽  
Group Formation ◽  
Social Identities ◽  
New Members ◽  
The Individual

Central to research in social psychology is the means in which communities form, attract new members, and develop over time. Research has found that the relative anonymity of Internet communication encourages self-expression and facilitates the formation of relationships based on shared values and beliefs. Self-expression in online social networks enables identity experimentation and development. As identities are fluid, situationally contingent, and are the perpetual subject and object of negotiation within the individual, the presented and perceived identity of the individual may not match reality. In this chapter, the authors consider the psychological challenges unique to understanding the dynamics of social identity formation and strategic interaction in online social networks. The psychological development of social identities in online social network interaction is discussed, highlighting how collective identity and self-categorization associates social identity to online group formation. The overall aim of this chapter is to explore how social identity affects the formation and development of online communities, how to analyze the development of these communities, and the implications such social networks have within education.

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