Forming an intention to act on recommendations given via online social networks

Online social networks (OSN) are technology-enabled relationship tools in which a user creates a profile, connects to other individuals, and shares user-generated content with them. One of the many features OSN offer is the ability to post messages in the form of product and service recommendations. Although word-of-mouth research has examined this type of personal communication to intentionally influence consumer decisions, limited understanding exists regarding when a user acts upon a recommendation received from one of their contacts. In this study, we draw on relationship theories and research on trust to analyze the impact relationship characteristics and behaviors of the OSN contacts have on trust in the recommendation that subsequently leads a user to act on this OSN contact’s recommendation. The results of a survey of 116 OSN users showed that closeness, excessive posting behavior, and past recommendation experience have a positive impact on trust and intention to act on the recommendation. One characteristic of the relationships, that is, similarity, however, does not affect recommendation trust in the context of OSN. The findings enhance our understanding of relationships and their defining characteristics in OSN. The results also provide insights into how companies might leverage OSN in their marketing efforts.     

Novel authentication procedures for preventing unauthorized access in social networks

Online Social Network (OSN) is the most widely used platform to communicate with one another. It is fast and cheap when compared to other modes of communication. However, Online Social Network profiles are being hacked often by employing various types of malicious attacks. Most of the times the attackers are successful in accessing the OSN account which illustrates that the present authentication mechanisms are not efficient enough. Once an online social network account is being hacked, it can be misused widely and the hacker makes the authorized user unable to login to the account by changing the login credentials. Security in OSN must be provided and it must prevent the attacks aimed by the attackers. However, to prevent unauthorized access in OSN a novel authentication procedure is needed and the novel authentication mechanism must abide the social network platform’s characteristics. This paper considers the attackers who aim to access the OSN account in an unauthorized manner and presents two novel authentication mechanisms to prevent the OSN platform. The proposed procedures make use of users’ chatting module statistics and relationship circle statistics to authenticate the users in Online Social Networks. This can be done by Multi kernel Fuzzy C-Means (MKFCM) Algorithm and Group Search Optimizer (GSO). These novel authentication procedures abide the OSN characteristics, as it makes use of Chatting and Relationship module.     

Competition and Information Deception in Online Social Networks

This study empirically examines the role of competition in determining intentions toward personal information deception (PID) among users of online social network (OSN) sites. PID refers to OSN users intentionally misrepresenting or refusing to disclose online personal information. The research proposes that consumers’ intentions toward PID depend on their desire for online competition with other OSN users, which in turn depends on user appraisals of available status and hedonic benefits, as well as established social norms around competition. An analysis of data gathered from 499 OSN participants (students enrolled at a state university in the southeastern United States) shows that competitive desires represent an important antecedent of PID behavior in OSN contexts. Theoretical and practical implications of the research are also discussed.     

Benevolence trust: a key determinant of user continuance use of online social networks

Online social networking (OSN) has attracted increased attention and growing membership in recent years. In this paper, we propose and test an extended and unified theory of acceptance and use of technology (UTAUT) model, including the additional areas of satisfaction, credibility trust, and benevolence trust, using an empirical survey of 676 OSN users to examine the influence of these factors. The results of regression analysis showed that the four key constructs of UTAUT (social influence, performance expectancy, effort expectancy, and facilitating conditions), as well as satisfaction, credibility trust, and benevolence trust, are all direct determinants of user continuance use of OSN. By comparing the coefficients of regression analysis, the relative importance of each determinant was also demonstrated. Results further indicate that benevolence trust has a much more significant effect on user continuance use of OSN than any other factor. A discussion is offered on the implications of these findings for OSN managers with regard to marketing and operations.     

Effect of online social networking on student academic performance

Online social networks (OSNs) have permeated all generations of Internet users, becoming a prominent communications tool, particularly in the student community. Thus, academic institutions and faculty are increasingly using social networking sites, such as Facebook and LinkedIn, to connect with current and potential students and to deliver instructional content. This has led to a rise in questions about the impact of OSN on academic performance and the possibility of using it as an effective teaching tool. To learn more about the impact on academic performance, we conducted a survey of business students at a large state university. Survey results were analyzed using structural equation modeling (SEM). The results revealed a statistically significant negative relationship between time spent by students on OSN and their academic performance. The time spent on OSN was found to be heavily influenced by the attention span of the students. Specifically, we determined that the higher the attention span, the lower is the time spent on OSN. Further, attention span was found to be highly correlated with characteristics that predict or influence student behavior, such as their perceptions about society’s view of social networking, their likes and dislikes of OSN, ease of use of OSN, etc.     

Customized Share Level Monitoring System for Users in OSN-Third Party Applications

Preserving privacy of the user is a very critical requirement to be met with all the international laws like GDPR, California privacy protection act and many other bills in place. On the other hand, Online Social Networks (OSN) has a wide spread recognition among the users, as a means of virtual communication. OSN may also acts as an identity provider for both internal and external applications. While it provides a simplified identification and authentication function to users across multiple applications, it also opens the users to a new spectrum of privacy threats. The privacy breaches costs to the users as well as to the OSN. Despite paying millions of dollars as fine every year, the OSN has not done any significant changes, as data is the fuel and what it loses as fine is far less compared to the money OSN makes out of the shared data. In this work, we have discussed a wide range of possible privacy threats and solutions prevailing in OSN-Third Party Application (TPA) data sharing scenario. Our solution models the behavior of the user, as well as TPA and pinpoints the avenues of over sharing to the users, thereby limiting the privacy loss of the user.     

应用随机游走的社交网络用户分类方法

针对现有在线社交网络用户分类方法不能有效利用用户属性和关系网络信息提高分类性能的问题,设计了一种基于随机游走模型的多标签分类方法MLCMRW。该方法的分类过程包括学习用户初始化类别标签以及通过迭代推理获得用户稳定标签分布两个阶段,并且其可以同时考虑用户属性以及关系网络特征信息进行分类。多个在线社交网络数据集上进行的实验表明,MLCMRW比其它已有的代表性方法有更好的分类性能,并且更适合对现实中的在线社交网络进行用户分类。     

Interplay between trust,information privacy concerns and behavioural intention of users on online social networks

Trust and privacy features of websites have evolved as an important concern for any businesses or interactions, particularly in online networks. The study investigates the relationship between trust, privacy concerns and behavioural intention of users on the social network. The behavioural intention of users on the online social network (OSN) is captured by intention to disclose information and intention to interact with others in OSN. The study was conducted on a sample of 457 active users from one of the major social networking website, Facebook. Partial least squares based structural equation modelling was used for analysing the results. The findings of the study reveal that intention to disclose information mediates the relationship between trust in the website and the intention to interact with others. Another important finding of the study indicates that prior positive experience with the website significantly impacts the trust in website, and the trust in website also plays a crucial role while determining the information privacy concerns in the OSN.     

OSN中基于分类器和改进n-gram模型的跨站脚本检测方法

针对在线社交网络中跨站脚本(XSS)攻击的安全问题,提出了一种在线社交网络恶意网页的检测方法。该方法依据在线社交网络中跨站脚本恶意代码的传播特性,提取一组基于相似性和差异性的特征,构造分类器和改进n-gram模型,再利用两种模型的组合,检测在线社交网络网页是否恶意。实验结果表明,与传统的分类器检测方法相比,结合了改进n-gram模型的检测方法保证了检测结果的可靠性,误报率约为5%。     

社交网络研究综述

社交网络已成为Web2.0时代最流行的应用,其服务范围已逐步从社交关系管理扩展到媒体信息、应用集成、电子商务等领域。社交网络中大量的活跃用户为研究网络行为、数据安全、信息传播以及其他跨学科问题提供了宝贵的数据和场景。自Facebook出现以来,研究者先后从不同的角度对社交网络进行了大量的研究,这些研究对人们认识社交网络内部规律、促进ICP服务改进具有重大意义。首先对社交网络的发展进行了简单的回顾;然后从社交网络的数据采集技术、社交网络用户行为分析、社交网络中的信息传播及社交网络中的用户隐私4个方面对已有的研究工作进行总结评价;最后,总结了当前研究中出现的问题并对未来研究发展趋势进行了展望。希望能为该领域的研究者提供一些有益的启示。     

Efficiency analysis of information technology and online social networks management: An integrated DEA-model assessment

This paper analyses the relationship between productive efficiency and online-social-networks (OSN) in Spanish telecommunications firms. A data-envelopment-analysis (DEA) is used and several indicators of business “social Media” activities are incorporated. A super-efficiency analysis and bootstrapping techniques are performed to increase the model's robustness and accuracy. Then, a logistic regression model is applied to characterise factors and drivers of good performance in OSN. Results reveal the company's ability to absorb and utilise OSNs as a key factor in improving the productive efficiency. This paper presents a model for assessing the strategic performance of the presence and activity in OSN.     

Design and analysis of a social botnet

Online Social Networks (OSNs) have attracted millions of active users and have become an integral part of today’s web ecosystem. Unfortunately, in the wrong hands, OSNs can be used to harvest private user data, distribute malware, control botnets, perform surveillance, spread misinformation, and even influence algorithmic trading. Usually, an adversary starts off by running an infiltration campaign using hijacked or adversary-owned OSN accounts, with an objective to connect with a large number of users in the targeted OSN. In this article, we evaluate how vulnerable OSNs are to a large-scale infiltration campaign run by socialbots: bots that control OSN accounts and mimic the actions of real users. We adopted the design of a traditional web-based botnet and built a prototype of a Socialbot Network (SbN): a group of coordinated programmable socialbots. We operated our prototype on Facebook for 8 weeks, and collected data about user behavior in response to a large-scale infiltration campaign. Our results show that (1) by exploiting known social behaviors of users, OSNs such as Facebook can be infiltrated with a success rate of up to 80%, (2) subject to user profile privacy settings, a successful infiltration can result in privacy breaches where even more private user data are exposed, (3) given the economics of today’s underground markets, running a large-scale infiltration campaign might be profitable but is still not particularly attractive as a sustainable and independent business, (4) the security of socially-aware systems that use or integrate OSN platforms can be at risk, given the infiltration capability of an adversary in OSNs, and (5) defending against malicious socialbots raises a set of challenges that relate to web automation, online-offline identity binding, and usable security.     

Concurrency control in distributed databases through time intervalsand short-term locks

A method for concurrency control in distributed database management systems that increases the level of concurrent execution of transactions, called ordering by serialization numbers (OSN), is proposed. The OSN method works in the certifier model and uses time-interval techniques in conjunction with short-term locks to provide serializability and prevent deadlocks. The scheduler is distributed, and the standard transaction execution policy is assumed, that is, the read and write operations are issued continuously during transaction execution. However, the write operations are copied into the database only when the transaction commits. The amount of concurrency provided by the OSN method is demonstrated by log classification. It is shown that the OSN method provides more concurrency than basic timestamp ordering and two-phase locking methods and handles successfully some logs which cannot be handled by any of the past methods. The complexity analysis of the algorithm indicates that the method works in a reasonable amount of time     

在线社会网络用户的信息分享行为预测研究

在线社会网络中信息的传播路径包含着用户对内容、来源等的偏好信息,研究运用信息的传播路径来预测用户信息分享行为的方法。基于传播路径的信息过滤能力研究了信息在网络中的传播过程和信息传播路径的转换方法。运用基于关联规则的分类算法对在线社会网络中的信息分享行为进行预测。以新浪微博为例对微博用户的转发行为进行了预测,结果表明该方法对在线社会网络中的活跃用户的信息分享行为的预测具有较好的效果。     

How media choice affects learner interactions in distance learning classes

In this research we explore aspects of learning, social interaction and community across online learning, also known as distance learning, in higher education. We measure the impact of online social networking (OSN) software versus traditional learning management system (LMS) software. Guided by a theoretical model for how individuals learn and interact within online communities, we measure student perceptions of learning, social interaction and course community before and after our interventions. Survey instruments measure perceived learning, social interaction and community, which we further explore using social network analysis (SNA). Survey results identified that students experienced higher levels of perceived social interaction and course community and, overall, had higher levels of satisfaction with OSN software than those using LMS software. Along this line, SNA results corroborated that OSN software yielded a higher number of interactions, providing a more engaging learning experience.     

Assessment of subjective influence and trust with an online social network game

The deduction of influence and trust between two individuals only from objective data in online social networks (OSNs) is a rather vague approach. Subjective assessments via surveys produce better results, but are harder to conduct considering the vast amount of friendships of OSN users. This work presents a framework for personalized surveys on relationships in OSNs, which follows a gamification approach. A Facebook game was developed, which was used to subjectively assess social influence and interpersonal trust based on models from psychology. The results show that it is possible to obtain subjective opinions and (limited) objective data about relationships with an OSN game. Also an implicit assessment of influence and trust with subcategory questions is feasible in this case.     

Overlay网络上的覆盖节点的放置问题

Overlay服务网络是一种通用的服务框架，它利用覆盖网络技术来向用户提供各种各样的服务。该文在讨论Overlay服务网络的基础上，提出了覆盖节点放置的问题，对该问题进行了形式化，并提出了相应的算法，对算法进行了相应的仿真。     

面向服务组合的覆盖服务网络设计

提出了面向服务组合的覆盖服务网络(OSN)的设计方法。从节点放置和链路选择两方面考虑OSN的设计问题。给出了覆盖节点的放置模型，分别用不同算法求解。实验结果表明，Lagrangian松弛算法在寻优能力上高于贪婪算法和随机算法。用实验模拟了不同链路选择策略对OSN路由性能的影响，结果说明邻接连接拓扑结构的故障恢复率高、路由开销小，适合作为OSN的拓扑结构。     

Overlay网络上的服务放置问题

覆盖服务网络(OSN)为支持新型服务的提供了一种有效的方式。围绕OSN的拓扑结构这一核心问题,还存在两个方面的问题值得研究:覆盖节点的放置策略或选择问题以及OSN上的服务放置问题。论文分析了目前人们对于覆盖网络拓扑结构方面的研究概括,提出了覆盖节点的放置问题(ONPP)和覆盖网络上的服务放置问题(SPP),并建立了相关的优化模型。     

CP2: Cryptographic privacy protection framework for online social networks

In Online Social Networks (OSNs), users interact with each other by sharing their personal information. One of the concerns in OSNs is how user privacy is protected since the OSN providers have full control over users’ data. The OSN providers typically store users’ information permanently; the privacy controls embedded in OSNs offer few options to users for customizing and managing the dissipation of their data over the network. In this paper, we propose an efficient privacy protection framework for OSNs that can be used to protect the privacy of users’ data and their online social relationships from third parties. The recommended framework shifts the control over data sharing back to the users by providing them with flexible and dynamic access policies. We employ a public-key broadcast encryption scheme as the cryptographic tool for managing information sharing with a subset of a user’s friends. The privacy and complexity evaluations show the superiority of our approach over previous.