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.     

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.     

Exploring sharing patterns for video recommendation on YouTube-like social media

YouTube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Face-book-like online social networks (OSNs) have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSS videos are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users’ viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends have higher common interest and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for video recommendation. An autoencoder model is developed to learn the social similarity of different videos in terms of their sharing in OSNs. We, therefore, propose a similarity-based strategy to enhance video recommendation for YouTube-like social media. Evaluation results demonstrate that this strategy can remarkably improve the precision and recall of recommendations, as compared to other widely adopted strategies without social information.     

If it looks like a spammer and behaves like a spammer,it must be a spammer: analysis and detection of microblogging spam accounts

Spam in online social networks (OSNs) is a systemic problem that imposes a threat to these services in terms of undermining their value to advertisers and potential investors, as well as negatively affecting users’ engagement. As spammers continuously keep creating newer accounts and evasive techniques upon being caught, a deeper understanding of their spamming strategies is vital to the design of future social media defense mechanisms. In this work, we present a unique analysis of spam accounts in OSNs viewed through the lens of their behavioral characteristics. Our analysis includes over 100 million messages collected from Twitter over the course of 1 month. We show that there exist two behaviorally distinct categories of spammers and that they employ different spamming strategies. Then, we illustrate how users in these two categories demonstrate different individual properties as well as social interaction patterns. Finally, we analyze the detectability of spam accounts with respect to three categories of features, namely content attributes, social interactions, and profile properties.     

Cultural Effects on Use of Online Social Media for Health-Related Information Acquisition and Sharing in Taiwan

The aims of this study were to use the technology acceptance model to examine how the cultural characteristics of social media users in Taiwan affect their use of social media for acquiring and sharing health-related information and to examine how their use of online social media benefits their social relationships and health self-efficacy. The research model in this quantitative cross-sectional study was tested with data collected from 321 active Facebook users in Taiwan. All three cultural characteristics/dimensions considered in the research model (masculinity, collectivism, and uncertainty avoidance) significantly affected the perceived usefulness and the perceived ease of using the online social media platform. However, masculinity had a significant positive effect on perceived usefulness but not on perceived ease of use. These results imply that technology tools for people in high masculinity cultures should be designed to maximize the effectiveness of the technology for achieving goals rather than to maximize the ease of using the technology. On the other hand, the use of online social media for acquiring and sharing health-related information significantly affected the social relationships of users but not their health self-efficacy. The results of this study imply that participants in online communities share health-related information not only to enhance their health but also to form strong social connections. This study proposes a new construct of technology acceptance, acquisition, and sharing of health-related information and investigates its effects on social relationships and health self-efficacy.     

Dynamic evolution of collective emotions in social networks: a case study of Sina weibo

Emotion is a fundamental object of human existence and determined by a complex set of factors. With the rapid development of online social networks (OSNs), more and more people would like to express their emotion in OSNs, which provides wonderful opportunities to gain insight into how and why individual emotion is evolved in social network. In this paper, we focus on emotion dynamics in OSNs, and try to recognize the evolving process of collective emotions. As a basis of this research, we first construct a corpus and build an emotion classifier based on Bayes theory, and some effective strategies (entropy and salience) are introduced to improve the performance of our classifier, with which we can classify any Chinese tweet into a particular emotion with an accuracy as high as 82%. By analyzing the collective emotions in our sample networks in detail, we get some interesting findings, including a phenomenon of emotion synchronization between friends in OSNs, which offers good evidence for that human emotion can be spread from one person to another. Furthermore, we find that the number of friends has strong correlation with individual emotion. Based on those useful findings, we present a dynamic evolution model of collective emotions, in which both self-evolving process and mutual-evolving process are considered. To this end, extensive simulations on both real and artificial networks have been done to estimate the parameters of our emotion dynamic model, and we find that mutual-evolution plays a more important role than self-evolution in the distribution of collective emotions. As an application of our emotion dynamic model, we design an efficient strategy to control the collective emotions of the whole network by selecting seed users according to k-core rather than degree.     

The factors that affect the performance of open source software development – the perspective of social capital and expertise integration

Drawing on social capital theory, we develop a theoretical model aiming to explore how open source software (OSS) project effectiveness (in terms of team size, team effort and team's level of completion) is affected by expertise integration. This in turn is influenced by three types of social capital – relational capital, cognitive capital and structural capital. In addition, this study also examines two moderating effects – the impact of technical complexity on the relationship between cognitive capital and expertise integration, and of task interdependence on the relationship between expertise integration and task completion. Through a field survey of 160 OSS members from five Taiwanese communities, there is support for some of the proposed hypotheses. Both reciprocity and centrality affect expertise integration as expected, but the influence of commitment and cognitive capital (including expertise and tenure) on expertise integration is not significant. Finally, expertise integration affects both team size and team effort, which in turn jointly influence task completion. This research contributes to advancing theoretical understanding of the effectiveness of free OSS development as well as providing OSS practitioners with insight into how to leverage social capital for improving the performance of OSS development.     

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。     

Privacy protection of online social network users,against attribute inference attacks,through the use of a set of exhaustive rules

Neural Computing and Applications - A malicious data miner can infer users’ private information in online social networks (OSNs) by data mining the users’ disclosed information. By...     

Understanding the role of technology attractiveness in promoting social commerce engagement: Moderating effect of personal interest

The rapid growth and increasing convergence of social networking and e-commerce open up a new era of social commerce, wherein people are encouraged to engage in various social interactions that are conducive to commercial activities. However, current studies are limited in investigating the concept of social commerce engagement and the processes through which social commerce engagement is established. Drawing upon interpersonal attraction theory and relationship management perspective, this study proposes a research model to address the influences of technology attractiveness, which is composed of task, social, and physical attractiveness, on social commerce involvement and engagement. Considering that social interactions in social commerce community are often stimulated by users’ common interests in products and consumption activities, the moderating role of personal interest is further examined by applying personality literature to reveal how technology attractiveness and community involvement take effect in the social commerce context. Empirical results indicate that all the three aspects of technology attractiveness (i.e., task, social, and physical attractiveness) are positively associated with community involvement, which in turn affects social commerce engagement. In particular, involvement fully mediates the impact of physical attractiveness and partially mediates the effects of task and social attractiveness. Personal interest enhances the effect of social attractiveness, whereas it weakens the effect of physical attractiveness on community involvement. Personal interest also strengthens the positive relationship between community involvement and social commerce engagement. Findings emerged from this study will contribute to the current understanding of how social commerce engagement is formed and help practitioners improve community attractiveness and deliver differential attractiveness to users with different levels of personal interest.     

Privacy as information access and illusory control: The case of the Facebook News Feed privacy outcry

Increasingly, millions of people, especially youth, post personal information in online social networks (OSNs). In September 2006, one of the most popular sites—Facebook.com—introduced the features of News Feed and Mini Feed, revealing no more information than before, but resulting in immediate criticism from users. To investigate the privacy controversy, we conducted a survey among 172 current Facebook users in a large US university to explore their usage behaviors and privacy attitudes toward the introduction of the controversial News Feed and Mini Feed features. We examined the degree to which users were upset by the changes, explored the reasons as to why, and examined the influences of the News Feed privacy outcry on user behavior changes. The results have demonstrated how an easier information access and an “illusory” loss of control prompted by the introduction of News Feed features, triggered users’ privacy concerns. In addition to enhancing our theoretical understanding of privacy issues in the online social networks, this research is also potentially useful to privacy advocates, regulatory bodies, service providers, and marketers to help shape or justify their decisions concerning the online social networks.     

Understanding the Intention of Using Mobile Social Networking Apps Across Cultures

Understanding the user acceptance of mobile social networking apps in different cultures can provide powerful insights for managers and marketers of social networking apps to develop effective globalized and localized strategies to attract users worldwide. Following the theory of planned behavior, this study develops a research model of privacy concern (PC), privacy risk (PR), and perceived enjoyment (PE) as attitudinal beliefs, subjective norm (SN) as normative belief, and smartphone self-efficacy (SE) as control belief to understand users’ intention to use mobile social networking apps. In particular, the impact of culture was investigated, considering the user base of mobile social networking apps is distributed globally and culturally diversified, and cultural values have direct impact on behavior. The research model was validated by survey data collected from 151 participants in the U.S. and 170 participants in South Korea. The data analysis results show that perceived enjoyment and subjective norm are the most important drivers behind users’ intention to use mobile social networking apps for both countries. No significant difference was found for the effects of privacy risk and subjective norm upon users’ intention to use mobile social networking apps across cultures. Implications of the findings upon theory and practice are discussed.     

Querying dynamic communities in online social networks简

Online social networks （OSNs） offer people the opportunity to join communities where they share a common interest or objective. This kind of community is useful for studying the human behavior, diffusion of information, and dynamics of groups. As the members of a community are always changing, an efficient solution is needed to query information in real time. This paper introduces the Follow Model to present the basic relationship between users in OSNs, and combines it with the MapReduce solution to develop new algorithms with parallel paradigms for querying. Two models for reverse relation and high-order relation of the users were implemented in the Hadoop system. Based on 75 GB message data and 26 GB relation network data from Twitter, a case study was realized using two dynamic discussion communities：#musicmonday and #beatcancer. The querying performance demonstrates that the new solution with the implementation in Hadoop significantly improves the ability to find useful information from OSNs.     

DiDuSoNet: A P2P architecture for distributed Dunbar-based social networks

Online Social Networks (OSNs) are becoming more and more popular on the Web. Distributed Online Social Networks (DOSNs) are OSNs which do not exploit a central server for storing users data and enable users to have more control on their profile content, ensuring a higher level of privacy. In a DOSN there are some technical challenges to face. One of the most important challenges is the data availability problem when a user is offline. In this paper we propose DiDuSoNet, a novel P2P Distributed Online Social Network where users can exercise full access control on their data. Our system exploits trust relationships for providing a set of important social services, such as trustness, information diffusion, and data availability. In this paper we show how our system manages the problem of data availability by proposing a new P2P dynamic trusted storage approach. By following the Dunbar concept, our system stores the data of a user only on a restricted number of friends which have regular contacts with him/her. Differently from other approaches, nodes chosen to keep data replicas are not statically defined but dynamically change according to users churn. In according to our previous work, we use only two online profile replicas at time. By using real Facebook data traces we prove that our approach offers high availability.     

An affordance perspective of enterprise social media and organizational socialization

In response to the challenge of socializing new IT employees, some IT departments are exploring the incorporation of enterprise social media (hereinafter ESM) as an informal organizational socialization tool. Because this is a relatively new phenomenon, little is known about how ESM facilitate employee socialization. In order to contribute to our understanding of how ESM affects employee socialization, this paper invokes a case study to explore how one organization’s implementation of an ESM for its IT new hire program influenced the socialization process and outcomes. To delve deeply into how the ESM influences socialization, we draw upon technology affordance theory to uncover the various first and second-order affordances actualized by different actor groups and the various outcomes resulting from the affordances. We then identify five generative mechanisms – bureaucracy circumvention, executive perspective, personal development, name recognition, and morale booster – that explain how the actualization of different strands of affordances by various groups of users produces eight different outcomes. Our results provide insights into the different affordances made possible by ESM in the context of a new hire socialization program and how these affordances have repercussions beyond those experienced by the individuals using the ESM. The results have important implications for new hire socialization and technology affordance research.     

The effects of generational differences on use continuance of Twitter: an investigation of digital natives and digital immigrants

This study links generational literature and information systems (IS) research by investigating the effect of generational differences on the usage of Twitter. Using theories of technology acceptance and IS continuance, we propose a research model to explore whether digital natives (DN) and digital immigrants (DI) perceive technology differently, and whether any such differences affect Twitter use-continuance behaviour. Structural equation modelling analysis of survey data from 385 users reveals that DN and DI perceive Twitter differently, providing partial support for the propositions of the model. The findings emphasise the role of generation in explaining users' continuance behaviour, with DN experiencing more social pressure to use Twitter, and finding it easier to use – but less useful – than do DI. This study has important implications for research in that it contributes to the debate on generational differences and to the IS continuance literature.     

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.     

A re-conceptualization of the interpretive flexibility of information technologies: redressing the balance between the social and the technical

Interpretive flexibility – the capacity of a specific technology to sustain divergent opinions – has long been recognized as playing an important role in explaining how technical artefacts are socially constructed. What is less clear is how a system's technical characteristics might limit its ability to be interpreted flexibly. This gap in the literature has largely arisen because recent contributions to this debate have tended to be rather one-sided, focussing almost solely upon the role of the human agent in shaping the technical artefact, and in so doing either downplaying or ignoring the artefact's shaping potential. The broad aim of this study was to reappraise the nature and role of interpretive flexibility but giving as much consideration to how an information system's technical characteristics might limit its ability to be interpreted flexibly, as we do to its potential for social construction. In this paper, we use the results of two in-depth case studies, in order to propose a re-conceptualization of the role of interpretive flexibility. In short, this model helps explain how the initial interpretations of stakeholders are significantly influenced by the scope and adaptability of the system's functionality. Stakeholder interpretations will then, in turn, influence how the system's functionality is appropriated and exploited by users, to allow divergent interpretations to be realized and sustained.     

A Cost-Efficient Approach to Storing Users’ Data for Online Social Networks

Journal of Computer Science and Technology - As users increasingly befriend others and interact online via their social media accounts, online social networks (OSNs) are expanding rapidly....     

Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest

Friend recommendation plays a key role in promoting user experience in online social networks (OSNs). However, existing studies usually neglect users’ fine-grained interest as well as the evolving feature of interest, which may cause unsuitable recommendation. In particular, some OSNs, such as the online learning community, even have little work on friend recommendation. To this end, we strive to improve friend recommendation with fine-grained evolving interest in this paper. We take the online learning community as an application scenario, which is a special type of OSNs for people to learn courses online. Learning partners can help improve learners’ learning effect and improve the attractiveness of platforms. We propose a learning partner recommendation framework based on the evolution of fine-grained learning interest (LPRF-E for short). We extract a sequence of learning interest tags that changes over time. Then, we explore the time feature to predict evolving learning interest. Next, we recommend learning partners by fine-grained interest similarity. We also refine the learning partner recommendation framework with users’ social influence (denoted as LPRF-F for differentiation). Extensive experiments on two real datasets crawled from Chinese University MOOC and Douban Book validate that the proposed LPRF-E and LPRF-F models achieve a high accuracy (i.e., approximate 50% improvements on the precision and the recall) and can recommend learning partners with high quality (e.g., more experienced and helpful).