Online Social Network Systems Research Articles

Re-Fake: Fake Account Classification in OSN Using RNN

Online Social Network (OSN) is an application for enabling public communication and sharing information. However, the fake account in the OSN can spread false information with an unknown source. It is a challenging task to detect malicious accounts in a large OSN system. The existence of fake accounts or unknown accounts on OSN can be a severe issue in data privacy-preserving. Various communities have proposed many techniques to deal with fake accounts in OSN, including rule-based black-white technique until learning approaches. Therefore, in this study we propose a classification model using the RNN to detect fake accounts accurately and effectively. We conduct this study in several steps, including gathering datasets, pre-processing, extraction, training our models using RNN. Based on the experiment result, our proposed model can produce a higher accuracy than the conventional learning model

DeepProfile: Finding fake profile in online social network using dynamic CNN

Online Social Networks (OSN) are popular applications for sharing various data, including text, photos, and videos. However, fake account problems are one of the obstacles in the current OSN systems. Attacker exploits fake accounts to distribute misleading information such as malware, virus, or malicious URLs. Inspired by the big successes of deep learning in computer vision, mainly in automatic feature extraction and representation, we propose DeepProfile, a deep neural network (DNN) algorithm to deal with fake account issues. Instead of using standard machine learning, we construct a dynamic CNN to train a learning model in fake profile classification. Notably, we propose a novel pooling layer to optimize the neural network performance in the training process. Demonstrated by the experiments, we harvest a promising result with better accuracy and small loss than common learning algorithms in a malicious account classification task.

QuickPoint: Efficiently Identifying Densest Sub-Graphs in Online Social Networks for Event Stream Dissemination

Efficient event stream dissemination is a challenging problem in large-scale Online Social Network (OSN) systems due to the costly inter-server communications caused by the per-user view data storage. To solve the problem, previous schemes mainly explore the structures of social graphs to reduce the inter-server traffic. Based on the observation of high cluster coefficients in OSNs, a state-of-the-art social piggyback scheme can save redundant messages by exploiting an intrinsic hub-structure in an OSN graph for message piggybacking. Essentially, finding the best hub-structure for piggybacking is equivalent to finding a variation of the densest sub-graph. The existing scheme computes the best hub-structure by iteratively removing the node with the minimum weighted degree. Such a scheme incurs a worst computation cost of $O(n^2)$ O ( n 2 ) , making it not scalable to large-scale OSN graphs. Using alternative hub-structure instead of the best hub-structure can speed up the piggyback assignment. However, they greatly sacrifice the communication efficiency of the assignment schedule. Different from the existing designs, in this work, we propose a QuickPoint algorithm, which removes a fraction of nodes in each iteration in finding the best hub-structure. We mathematically prove that QuickPoint converges in $O(log_an) (a>1)$ O ( l o g a n ) ( a > 1 ) iterations in finding the best hub-structure for efficient piggyback. We implement QuickPoint in parallel atop Pregel, a vertex-centric distributed graph processing platform. Comprehensive experiments using large-scale data from Twitter and Flickr show that our scheme is 38.8× more efficient compared to existing schemes.

Privacy Analysis on Microblogging Online Social Networks

Online Social Networks (OSNs) incorporate different forms of interactive communication, including microblogging services, multimedia sharing, business networking, and so on. They allow users to create profiles, connect with friends, and share their daily activities and thoughts. However, this ease of use of OSNs comes with a cost in terms of users’ privacy and security. The big amount of personal data shared in the users’ profiles or correlated from their activities can be stored, processed, and sold for advertisement or statistical purposes. It attracts also malicious users who can collect and exploit the data and target different types of attacks. In this article, we review the state of the art of OSNs existing either in the literature or deployed for use. We focus on the OSN systems that offer, but not exclusively, microblogging services. We analyze and evaluate each system based on a set of characteristics, and we compare them based on their usability and the level of protection of privacy and security they provide. This study is a first step toward understanding the security and privacy controls and measuring their level in an OSN.

Piggyback Game: Efficient Event Stream Dissemination in Online Social Network Systems

Event stream dissemination dominates the workloads in large-scale Online Social Network (OSN) systems. Based on the de facto per-user view data storage, event stream dissemination raises a large amount of inter-server traffic due to the complex interconnection among OSN users. The state-of-the-art schemes mainly explore the structure features of social graphs to reduce the inter-server communications for event stream dissemination. Different sub-graph structures are exploited for achieving approximated optimal solutions. However, such schemes incur prohibitively high cost of either computation or communication. In this work, we follow a different design philosophy by using a game theoretic approach, which decomposes the highly complex graph computation problem into rational decision making of every individual social link. Specifically, we propose a novel social piggyback game to achieve a more efficient solution. We mathematically prove the existence of the Nash Equilibrium of the social piggyback game. Moreover, we propose an efficient best response dynamic algorithm to achieve the Nash Equilibrium, which quickly converges in a small number of iterations for large-scale OSNs. We further show that the communication cost of this design achieves a 1.5-approximation of the theoretical social optimum. We conduct comprehensive experiments using large-scale real-world traces from popular OSN systems as well as implement a prototype system to evaluate the performance of this design. Results show that the social piggyback game achieves a significant 302× improvement in system efficiency compared to existing schemes.

A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics.

We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an "S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems.

A challenge-response mechanism for securing online social networks against social bots

Social bots is fast becoming a serious security threat to online social networks (OSNs). Social bots are automated software tools able to execute malicious activities in OSNs systems in an automated fashion. It can perform auto-sharing and posting, sending fake friend requests, harvesting private information, etc. There is evidence that social bots play a crucial role in penetrating privacy and security of social networks. Hence, these malicious software tools represent a big security challenge against social network service provider (SNSP). In this paper, we introduce a novel antibot mechanism called Necklace CAPTCHA for securing OSNs platforms against the automated behaviours of social bots. Necklace CAPTCHA is an Image-based CAPTCHA, which utilises the functionality of Necklace Graph approach to generate its challenge-response tests in a novel fashion. The results demonstrated that Necklace CAPTCHA is an effective and secure anti-bot mechanism compared with other CAPTCHAs in the literature with respect to the usability and security metrics.

A challenge-response mechanism for securing online social networks against social bots

Social bots is fast becoming a serious security threat to online social networks (OSNs). Social bots are automated software tools able to execute malicious activities in OSNs systems in an automated fashion. It can perform auto-sharing and posting, sending fake friend requests, harvesting private information, etc. There is evidence that social bots play a crucial role in penetrating privacy and security of social networks. Hence, these malicious software tools represent a big security challenge against social network service provider (SNSP). In this paper, we introduce a novel antibot mechanism called Necklace CAPTCHA for securing OSNs platforms against the automated behaviours of social bots. Necklace CAPTCHA is an Image-based CAPTCHA, which utilises the functionality of Necklace Graph approach to generate its challenge-response tests in a novel fashion. The results demonstrated that Necklace CAPTCHA is an effective and secure anti-bot mechanism compared with other CAPTCHAs in the literature with respect to the usability and security metrics.

Social media platforms as a photo-elicitation tool in research on alcohol intoxication and gender.

Aim:This methodological article aims to describe the use of comparative social media platforms within a photo-elicitation (PE) activity as part of a multi-method interview-based study on the gendered meanings of alcohol intoxication among young adults (aged 18–25 years, n = 200).Method:Early interviews revealed social media as a particularly engaging topic for participants, and discussions of social media exposed relevant issues that often were not discussed in other sections of the interview guide. By embedding photos of young people drinking within three social media platforms with photo-sharing capabilities – Instagram, SnapChat and Facebook – we elicited narrative data revealing important aspects of the meanings of intoxication and providing information on how participants manage and judge drinking behaviours shared through online social networking systems (SNS).Conclusions:Given that social media use and photo-sharing are so common, familiar, and endemic among young people, to ignore this feature of contemporary social interaction would have limited our exploration of meanings of intoxication. We suggest that embedding existing methods, such as photo-elicitation, into social media contexts that are salient for youth may be a valuable strategy for providing a more comprehensive approach for investigating contemporary social issues.

Participation in Online Social Networks

Why and how more and more people get involved and use social networking systems are critical topics in social network analysis (SNA). As a matter of fact, social networking systems bring online a growing number of acquaintances, for many different purposes. Both business interests and personal recreational goals are motivations for using online social networks (OSN) or other social networking systems. The participation in social networks is a phenomenon which has been studied with several theories, and SNA is useful for common business problems, e.g., launching distributed teams, retaining people with vital knowledge for the organization, improving access to knowledge and spreading ideas and innovation. Nevertheless, there are some difficulties, such as anti-social behaviors of participants, lack of incentives, organizational costs and risks. In this article, a survey of the basic features of SNA, participation theories and models are discussed, with emphasis on social capital, information spreading, motivations for participation, and anti-social behaviors of social network users.

QoS enhanced distributed load balancing and task scheduling framework for wireless networks using hybrid optimisation algorithm

Quality of service (QoS) is the main challenging issue in load balancing, task scheduling process in dynamic systems, such as grid system, peer-to-peer system, ad hoc networks, cloud computing system, pervasive computing system, and online social network system. The inefficient task scheduling process reduces QoS parameters, such as energy consumption, throughput, network lifetime, deadline missing ratio and schedule delay time. In this paper, we proposed QoS enhanced distributed load balancing, task scheduling frameworks for wireless networks (WNs) using hybrid optimisation algorithm (EDFHOA). We improve QoS of distributed frameworks by two phase system through an efficient clustering methodology using particle swarm optimisation (PSO) algorithm for load balancing in the first phase. A cuckoo search (CS) algorithm with load balanced for task scheduling in the second phase. The proposed two-phase hybrid algorithms enhance task scheduling, such as latency experienced by the balanced clustering, energy consumption, throughput, network lifetime, efficient resource utilisation and less processing time requirement and highly accurate than fault tolerant task allocation algorithm (FTAOA).

A Provably Secure Group Key Agreement Scheme With Privacy Preservation for Online Social Networks Using Extended Chaotic Maps

With the rapid growth in the popularity of mobile devices and development of network technologies, various online social networking applications have grown in popularity. While these social networking sites provide benefits in terms of enhanced connectivity with people all around the world, they can also pose security threats and raise privacy concerns due to their vulnerability to be exploited by malicious agents. Such social networking sites where members can transmit or share their social information via public channels increase the risk that these information may be exposed to unwanted users. Therefore, it is important to enhance the security of these online social network services using group session keys. These group session keys also need to be updated in case a new member joins the group or an old member leaves the social group. To enhance the trustworthiness of the online social network systems, in this paper, we propose a secure chaotic maps-based group key agreement scheme. In this proposed scheme, we also provide member anonymity to ensure the privacy of the communication between the social networking platform and the members. The proposed solution does not rely on a centralized online key center or a trusted group chairman, thus ensuring fairness. We integrate the mechanisms of message encryption and member verification into the scheme to allow the members to anonymously interact with the services of the online social network. We verify the formal security of the proposed solution using the widely accepted BAN logic analysis and simulation verification with Proverif to prove that our scheme is secure against both passive and active attacks. We also demonstrate that the proposed scheme is efficient in its implementation and achieves greater functionality criteria in comparison with similar existing proposals.

CBL: Exploiting Community Based Locality for Efficient Content Search Service in Online Social Networks

Retrieving relevant data for users in online social network (OSN) systems is a challenging problem. Cassandra, a storage system used by popular OSN systems, such as Facebook and Twitter, relies on a DHT-based scheme to randomly partition the personal data of users among servers across multiple data centers. Although DHT is highly scalable for hosting a large number of users (personal data), it leads to costly inter-server communications across data centers due to the complex interconnection and interaction among OSN users. In this paper, we explore how to retrieve the OSN content in a cost-effective way by retaining the simple and robust nature of OSNs. Our approach exploits a simple, yet powerful principle called Community-Based Locality (CBL), which posits that if a user has a one-hop neighbor within a particular community, it is very likely that the user has other one-hop neighbors inside the same community. We demonstrate the existence of community-based locality in diverse traces of popular OSN systems such as Facebook, Orkut, Flickr, Youtube, and Livejournal. Based on the observation, we design a CBL-based algorithm to build the content index in OSN systems. By partitioning and indexing the relevant data of users within a community on the same server in the data center, the CBL-based index avoids a significant amount of inter-server communications during searching, making retrieving relevant data for a user in large-scale OSNs efficient. In addition, by using CBL-based scheme we can provide much faster search response and balanced loads. We conduct comprehensive trace-driven simulations to evaluate the performance of the proposed scheme. Results show that ourscheme significantly reduces the network traffic by 73 percent while reduces the query latency by 35 percent compared with existing schemes.

Cost-Optimized Microblog Distribution over Geo-Distributed Data Centers

The unprecedent growth of microblog services poses significant challenges on network traffic and service latency to the underlay infrastructure (i.e., geo-distributed data centers). Furthermore, the dynamic evolution in microblog status generates a huge workload on data consistence maintenance. In this article, motivated by insights of cross-media analysis-based propagation patterns, we propose a novel cache strategy for microblog service systems to reduce the inter-data center traffic and consistence maintenance cost, while achieving low service latency. Specifically, we first present a microblog classification method, which utilizes the external knowledge from correlated domains, to categorize microblogs. Then we conduct a large-scale measurement on a representative online social network system to study the category-based propagation diversity on region and time scales. These insights illustrate social common habits on creating and consuming microblogs and further motivate our architecture design. Finally, we formulate the content cache problem as a constrained optimization problem. By jointly using the Lyapunov optimization framework and simplex gradient method, we find the optimal online control strategy. Extensive trace-driven experiments further demonstrate that our algorithm reduces the system cost by 24.5% against traditional approaches with the same service latency.

Activity correlation spectroscopy: a novel method for inferring social relationships from activity data

Inferring social relationships among individuals is a difficult problem, typically requiring either intrusive measurements (e.g., surveys) or convenience samples (e.g., ties captured by online social networking systems). Automatically collected activity data from mobile devices or similar sources provide a promising alternative for inexpensive and unobtrusive detection of interpersonal relationships. Here, we introduce a new method—activity correlation spectroscopy—for inferring relationships by exploiting the spectral and distributional structure of activity correlation within dyads. Unlike existing techniques, our approach can be employed with minimal, individual-level (i.e., non-relational), and non-identifying data that are easily collected using commodity hardware. We demonstrate our methodology via an application to detection of friendship and group co-membership using mobile device and survey data from the MIT Reality Mining study (Eagle et al. in Proc Natl Acad Sci 106:15274–15278, 2009).

An Access Control Model for Online Social Networks Using User-to-User Relationships

Users and resources in online social networks (OSNs) are interconnected via various types of relationships. In particular, user-to-user relationships form the basis of the OSN structure, and play a significant role in specifying and enforcing access control. Individual users and the OSN provider should be enabled to specify which access can be granted in terms of existing relationships. In this paper, we propose a novel user-to-user relationship-based access control (UURAC) model for OSN systems that utilizes regular expression notation for such policy specification. Access control policies on users and resources are composed in terms of requested action, multiple relationship types, the starting point of the evaluation, and the number of hops on the path. We present two path checking algorithms to determine whether the required relationship path between users for a given access request exists. We validate the feasibility of our approach by implementing a prototype system and evaluating the performance of these two algorithms.

Minimizing Inter-Server Communications by Exploiting Self-Similarity in Online Social Networks

Efficiently operating on relevant data for users in large-scale online social network (OSN) systems is a challenging problem. Storage systems used by popular OSNs often rely on key-value stores, where randomly partitioning the data of users among servers across the data centers is the defacto standard. Although by using DHTs, the random partition scheme is highly scalable for hosting a large number of users, it leads to costly inter-server communications across data centers due to the complexity of interconnection and interaction between OSN users. In this paper, we explore how to reduce the inter-server communications by retaining the simple and robust nature of OSNs. We propose a data placement solution atop OSN systems to divide users among servers according to the interaction-locality-based structure. Our approach exploits a simple, yet powerful principle of OSN interactions, self-similarity, which reveals that the inter-server communication cost is minimized under such intrinsic structure. Our algorithm avoids a significant amount of inter-server traffic as well as achieves load balance among servers across the data centers. We demonstrate the existence of self-similarity in large-scale Facebook traces including 10 million Facebook users and 24 million interaction events. We conduct comprehensive trace-driven simulations to evaluate this design. Results show that our scheme significantly reduces the traffic and latency of OSN systems comparing to existing schemes.

English

The purpose of this paper is to discuss the application of tag clouding for information visualisation and information retrieval over social cataloguing tool such as Shelfari (http://shelfari.com). This is a case study where tag cloud is being used as a tool for information visualisation and information retrieval to create awareness about new arrivals in the library. For the purpose of creating awareness amongst the user of the library and promote the use of newly catalogued resources, an online social cataloguing system ‘Shelfari’ (http://shelfari.com) has been used. The new arrivals are catalogued through social cataloguing system. The Library of Congress subject heading (LCSH) and Medical Subject Headings (MeSH) are being used as tag words for each resource. The tag cloud created in this way has been evaluated for the effectiveness of the social cataloguing. The results suggest that tag clouding helps to create a visual effect and help users to learn about availability of the resources in the library along with the collaborative activities such as reviews and feedbacks, to choose right kind of reading materials. Social cataloguing activities are now getting momentum in online social network system. The use of internet and social network activities are increasing day-by-day so as the use of reviews and feedback for choosing right reading material. This study shall be valuable for those who are interested in choosing right reading materials based on popularity. 10.14429/djlit.35.1.8036

The Impact Facebook and Twitter has on the Cognitive Social Capital of University Students

The impact that Facebook and Twitter usage has on the creation and maintenance of university student’s cognitive social capital was investigated on students in the Western Cape province of South Africa. Facebook and Twitter were selected as part of the research context because both are popular online social network systems (SNSs), and few studies were found that investigated the impact that both Facebook and Twitter have on the cognitive social capital of South African university students. Data was collected from a survey questionnaire, which was successfully completed by over 100 students from all 5 universities within the Western Cape. The questionnaire was obtained from a previous study, allowing comparisons to be made. Analysis of the results however, did not show a strong relationship between the intensity of Facebook and Twitter usage, and the various forms of social capital. Facebook usage was found to correlate with student’s satisfaction with university life; which suggests that increasing the intensity of Facebook usage for students experiencing low satisfaction with university life might be beneficial.

Propagation-based social-aware multimedia content distribution

Online social networks have reshaped how multimedia contents are generated, distributed, and consumed on today's Internet. Given the massive number of user-generated contents shared in online social networks, users are moving to directly access these contents in their preferred social network services. It is intriguing to study the service provision of social contents for global users with satisfactory quality of experience. In this article, we conduct large-scale measurement of a real-world online social network system to study the social content propagation. We have observed important propagation patterns, including social locality, geographical locality, and temporal locality. Motivated by the measurement insights, we propose a propagation-based social-aware delivery framework using a hybrid edge-cloud and peer-assisted architecture. We also design replication strategies for the architecture based on three propagation predictors designed by jointly considering user, content, and context information. In particular, we design a propagation region predictor and a global audience predictor to guide how the edge-cloud servers backup the contents, and a local audience predictor to guide how peers cache the contents for their friends. Our trace-driven experiments further demonstrate the effectiveness and superiority of our design.