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.     

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.     

Transitive node similarity: predicting and recommending links in signed social networks

Online social networks (OSNs) like Facebook, Myspace, and Hi5 have become popular, because they allow users to easily share content. OSNs recommend new friends to registered users based on local features of the graph (i.e., based on the number of common friends that two users share). However, OSNs do not exploit the whole structure of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we define a basic node similarity measure that captures effectively local graph features (i.e., by measuring proximity between nodes). We exploit global graph features (i.e., by weighting paths that connect two nodes) introducing transitive node similarity. We also derive variants of our method that apply to different types of networks (directed/undirected and signed/unsigned). We perform extensive experimental comparison of the proposed method against existing recommendation algorithms using synthetic and real data sets (Facebook, Hi5 and Epinions). Our experimental results show that our FriendTNS algorithm outperforms other approaches in terms of accuracy and it is also time efficient. Finally, we show that a significant accuracy improvement can be gained by using information about both positive and negative edges.     

一种基于P2P的短视频分享系统

现有在线短视频分享策略通常采用C/S架构,给视频服务器带来较大的带宽压力。为此,提出一种采用点对点方式的在线短视频分享系统IShare,该系统结合用户点播偏好和视频文件之间的社会网络特性实现视频分享。IShare主要包括基于点播兴趣的节点分簇和视频数据源节点的查找2个核心技术。实验结果表明,IShare具备较好的视频数据源节点查找能力,可降低视频服务器带宽资源消耗。     

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....     

Catching a viral video

The sharing and re-sharing of videos on social sites, blogs e-mail, and other means has given rise to the phenomenon of viral videos—videos that become popular through internet sharing. In this paper we seek to better understand viral videos on YouTube by analyzing sharing and its relationship to video popularity using millions of YouTube videos. The socialness of a video is quantified by classifying the referrer sources for video views as social (e.g. an emailed link, Facebook referral) or non-social (e.g. a link from related videos). We find that viewership patterns of highly social videos are very different from less social videos. For example, the highly social videos rise to, and fall from, their peak popularity more quickly than less social videos. We also find that not all highly social videos become popular, and not all popular videos are highly social. By using our insights on viral videos we are able develop a method for ranking blogs and websites on their ability to spread viral videos.     

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。     

Analysis of MySpace user profiles

Online social networks have attracted millions of users, who have integrated social network web sites into their daily life. Users participate to the changes and to the evolution of these sites because they are producers and reviewers of contents that help them to maintain the existing social relationships, make new friends, collaborate and enrich experiences. This paper presents a study of the characteristics of the users of MySpace web site, with the objective of studying relationships and interactions among users and deriving hints about their behavior. The analysis relies on data collected by monitoring the web site for 12 weeks. Typical user behaviors have been derived and classes of users characterized by different levels of participation to the social network have been identified. In particular, the analysis reveals that most of the users actively participate to the social network and specify many personal details. Social networks web sites allow access to such details; the sharing of information about users and their relationships can lead to non-ethic online activities, which threat the privacy and the security of users themselves.     

Robust wireless sharing of internet video streams

The gateways are the performance bottleneck of wireless mesh access networks and thus alleviating stress on them is essential to making such wireless networks robust and scalable. Using proxy servers or wireless peer-to-peer streaming techniques can help reduce the gateway load. However, these techniques, because they are data caching methods, do not save wireless resources. We instead consider a communication-sharing approach in this paper. Traditional stream sharing solutions depend on cooperation with the video server. However, in the wireless access network it is difficult to cooperate with online video sites. To address this problem in wireless mesh access networks, we propose a distributed video sharing technique called Dynamic Stream Merging (DSM). DSM is able to improve the robustness of the access network without cooperation from the online video site or the users and has the intelligence to handle sudden spikes in demand for certain videos due to specific events, thereby preventing adverse effects to other daily wireless traffic. The technique can also leverage the 80:20 data access pattern, common for many video applications, to substantially increase the service throughput. We explain the DSM technique, present the system prototype, and discuss the experimental results.     

Publicly Private and Privately Public: Social Networking on YouTube

YouTube is a public video-sharing website where people can experience varying degrees of engagement with videos, ranging from casual viewing to sharing videos in order to maintain social relationships. Based on a one-year ethnographic project, this article analyzes how YouTube participants developed and maintained social networks by manipulating physical and interpretive access to their videos. The analysis reveals how circulating and sharing videos reflects different social relationships among youth. It also identifies varying degrees of "publicness" in video sharing. Some participants exhibited "publicly private" behavior, in which video makers' identities were revealed, but content was relatively private because it was not widely accessed. In contrast, "privately public" behavior involved sharing widely accessible content with many viewers, while limiting access to detailed information about video producers' identities.     

融合用户社会地位和矩阵分解的推荐算法

随着社交网络服务的日益流行,社交网络平台为推荐算法提供了丰富的额外信息.假设朋友之间共享更多的共同偏好并且用户往往易于接受来自朋友的推荐,越来越多的推荐系统利用社交网络中用户之间的信任关系来改进传统推荐算法的性能.然而,现有基于社交网络推荐算法忽略了2个问题：1)在不同的领域中,用户信任不同的朋友;2)由于用户在不同的领域内具有不同的社会地位,因此,用户在不同的领域内受朋友的影响程度是不同的.首先利用整体的社交网络结构信息和用户的评分信息推导特定领域社交网络结构,然后利用PageRank算法计算用户在特定领域的社会地位,最后提出了一种融合用户社会地位信息的矩阵分解推荐算法.在真实数据集上的实验结果表明：融合用户地位信息的矩阵分解推荐算法的性能优于传统的基于社交网络推荐算法.     

Exploring the power of social hub services

World Wide Web - Given the diverse focuses of emerging online social networks (OSNs), it is common that a user has signed up on multiple OSNs. Social hub services, a.k.a., social directory...     

Information propagation in online social networks: a tie-strength perspective

In this paper, we investigate the relationship between the tie strength and information propagation in online social networks (OSNs). Specifically, we propose a novel information diffusion model to simulate the information propagation in OSNs. Empirical studies through this model on various real-world online social network data sets reveal three interesting findings. First, it is the adoption of the information pushing mechanism that greatly facilitates the information propagation in OSNs. Second, some global but cost-intensive strategies, such as selecting the ties of higher betweenness centralities for information propagation, no longer have significant advantages. Third, the random selection strategy is more efficient than selecting the strong ties for information propagation in OSNs. Along this line, we provide further explanations by categorizing weak ties into positive and negative ones and reveal the special bridge effect of positive weak ties. The inverse quantitative relationship between weak ties and network clustering coefficients is also carefully studied, which finally gives reasonable explanations to the above findings. Finally, we give some business suggestions for the cost-efficient and secured information propagation in online social networks.     

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...     

IFCS: an intelligent fast channel switching in IPTV over PON based on human behavior prediction

In this work we are concerned with detecting non-collaborative videos in video sharing social networks. Specifically, we investigate how much visual content-based analysis can aid in detecting ballot stuffing and spam videos in threads of video responses. That is a very challenging task, because of the high-level semantic concepts involved; of the assorted nature of social networks, preventing the use of constrained a priori information; and, which is paramount, of the context-dependent nature of non-collaborative videos. Content filtering for social networks is an increasingly demanded task: due to their popularity, the number of abuses also tends to increase, annoying the user and disrupting their services. We propose two approaches, each one better adapted to a specific non-collaborative action: ballot stuffing, which tries to inflate the popularity of a given video by giving “fake” responses to it, and spamming, which tries to insert a non-related video as a response in popular videos. We endorse the use of low-level features combined into higher-level features representation, like bag-of-visual-features and latent semantic analysis. Our experiments show the feasibility of the proposed approaches.     

CStream: neighborhood bandwidth aggregation for better video streaming

Despite the popularity of watching videos online, challenges still remain in video streaming in many scenarios. Limited home broadband and mobile phone 3G bandwidths mean many users stream videos at compromised quality. To provide additional bandwidth for streaming, we propose CStream, a system that aggregates bandwidth from multiple cooperating users in a neighborhood environment for better video streaming. CStream exploits the fact that wireless devices have multiple network interfaces and connects cooperating users with a wireless ad-hoc network to aggregate their unused downlink Internet bandwidth. CStream dynamically generates a streaming plan to stream a single video using multiple connections, continuously adapting to changes in the neighborhood and variations in the available bandwidth. CStream is developed and evaluated on a test bed of computers, allowing for a detailed, controlled evaluation of performance. Analysis of the results shows a linear increase in throughput over single-connection streaming and improved video quality as the number of cooperating users in a neighborhood increase.     

Private discovery of common social contacts

Digital services that are offered, and consumed, on the basis of social relationships form the backbone of social clouds—an emerging new concept that finds its roots in online social networks. The latter have already taken an essential role in people’s daily life, helping users to build and reflect their social relationships to other participants. A key step in establishing new links entails the reconciliation of shared contacts and friends. However, for many individuals, personal relationships belong to the private sphere, and, as such, should be concealed from potentially prying eyes of strangers. Consequently, the transition toward social clouds cannot set aside mechanisms to control the disclosure of social links. This paper motivates and introduces the concept of Private Discovery of Common Social Contacts, which allows two users to assess their social proximity through interaction and learn the set of contacts (e.g., friends) that are common to both users, while hiding contacts that they do not share. We realize private contact discovery using a new cryptographic primitive, called contact discovery scheme (CDS), whose functionality and privacy is formalized in this work. To this end, we define a novel privacy feature, called contact-hiding, that captures our strong privacy goals. We also propose the concept of contact certification and show that it is essential to thwart impersonation attacks on social relationships. We build provably private and realistically efficient CDS protocols for private discovery of mutual contacts. Our constructions do not rely on a trusted third party (TTP)—all contacts are managed independently by the users. The practicality of our proposals is confirmed both analytically and experimentally on different computing platforms. We show that they can be efficiently deployed on smartphones, thus allowing ad hoc and ubiquitous contact discovery outside of existing social networks. Our CDS constructions allow users to select their (certified) contacts to be included in individual protocol executions. That is, users may perform context-dependent contact discovery using any subset (circle) of their contacts.     

Towards understanding the gamification upon users’ scores in a location-based social network

Online social platform, such as Wikipedia and Foursquare, has been increasingly exploded due to not only various useful services provided but also social gaming mechanisms that can keep users actively engaged. For example, users are awarded ”virtual goods” like badges and points when they contribute to the community in the network by voluntarily sharing ideas and other information. In this paper, we aim to examine the effectiveness of a social gamification mechanism, named user scores, designed in Foursquare which is one of most popular location-based social networks. A user’s score in Foursquare is an aggregate measure based on recent check-in activities of the user, which reflects a snapshot summary of the user’s temporal and spatial behaviors. Whenever a user checks in to a venue, a list of scores of the user’s friends are visible to the user via a ”leaderboard” which ranks these users’ scores in a descending order. Given a pair of friends who participate in a score competition in such a gimification mechanism, we identify if one user’s scores have significant influence on the other user’s scores by utilizing the Granger Causality Test. To understand what types of users and what types of friends tend to participate in the score competition (i.e., their check-ins are more likely driven by such a gamification mechanism), we extract users’ features (e.g. user’s degree) as well as the features of pairs of friends (e.g., number of common friends, score similarity and ranking difference) to examine whether these features have correlations with those pairs of users who are identified as being involved in the score game. The identified influence on user scores has the important implication on applications including friend and venue recommendations in location-based social networks.     

Motivating participation in social computing applications: a user modeling perspective

The explosive growth of Web-based social applications over the last 10?years has led people to engage in online communities for various purposes: to work, learn, play, share time and mementos with friends and family and engage in public action. Social Computing Applications (SCA) allow users to discuss various topics in online forums, share their thoughts in blogs, share photos, videos, bookmarks, and connect with friends through social networks. Yet, the design of successful social applications that attract and sustain active contribution by their users still remains more of an art than a science. My research over the last 10?years has been based on the hypothesis that it is possible to incorporate mechanisms and tools in the design of the social application that can motivate users to participate, and more generally, to change their behavior in a desirable way, which is beneficial for the community. Since different people are motivated by different things, it can be expected that personalizing the incentives and the way the rewards are presented to the individual, would be beneficial. Also since communities have different needs in different phases of their existence, it is necessary to model the changing needs of communities and adapt the incentive mechanisms accordingly, to attract the kind of contributions that are beneficial. Therefore User and Group (Community) Modeling is an important area in the design of incentive mechanisms. This paper presents an overview of different approaches to motivate users to participate. These approaches are based on various theories from the area of social psychology and behavioral economics and involve rewards mechanisms, reputation, open group user modeling, and social visualization. Future trends are outlined towards convergence with the areas of persuasive systems design, adaptive/personalized systems, and intelligent social learning environments.     

Age differences in online social networking – A study of user profiles and the social capital divide among teenagers and older users in MySpace

The aim of this research was to investigate age differences and similarities in the use of the social networking website MySpace, to explore potential differences in social capital among older people (users over 60 years of age) compared to teenagers (users between 13 and 19 years of age). We used locally developed web crawlers to collect data from MySpace’s user profile pages, and to quantify any differences that exist in the networks of friends of older people and teenagers. Content analysis was applied to investigate differences in social activities between the two age groups on MySpace, and the way they represent themselves on their profile pages. Our findings show a social capital divide: teenagers have larger networks of friends compared to older users of MySpace. On the other hand, we found that the majority of teenage users’ friends are in their own age range (age ± 2 years), whilst older people’s networks of friends tend to have a more diverse age distribution. In addition, our results show that teenagers tend to make more use of different media (e.g. video, music) within MySpace and use more self-references and negative emotions when describing themselves on their profile compared to older people.