Friends In Online Social Networks Research Articles

Preference for Number of Friends in Online Social Networks

Preferences or dislikes for specific numbers are ubiquitous in human society. In traditional Chinese culture, people show special preference for some numbers, such as 6, 8, 10, 100, 200, etc. By analyzing the data of 6.8 million users of Sina Weibo, one of the largest online social media platforms in China, we discover that users exhibit a distinct preference for the number 200, i.e., a significant fraction of users prefer to follow 200 friends. This number, which is very close to the Dunbar number that predicts the cognitive limit on the number of stable social relationships, motivates us to investigate how the preference for numbers in traditional Chinese culture is reflected on social media. We systematically portray users who prefer 200 friends and analyze their several important social features, including activity, popularity, attention tendency, regional distribution, economic level, and education level. We find that the activity and popularity of users with the preference for the number 200 are relatively lower than others. They are more inclined to follow popular users, and their social portraits change relatively slowly. Besides, users who have a stronger preference for the number 200 are more likely to be located in regions with underdeveloped economies and education. That indicates users with the preference for the number 200 are likely to be vulnerable groups in society and are easily affected by opinion leaders.

OGBAC—A Group Based Access Control Framework for Information Sharing in Online Social Networks

Internet users receive various online social networks (OSNs) services, however, providers of OSNs do not always provide users fine-grained privacy protection mechanisms with sufficient privacy protection for shared resources. In this paper, we propose a formal Group-Based Access Control (oGBAC) framework for preventing privacy disclosure when sharing information within or among groups in OSNs. Our framework extends the group-centric Secure Information Sharing (g-SIS) models by adapting the concept of the group to OSNs. We impose some restrictions to the group and information flow among groups to ensure that operations cannot incur privacy disclosure when sharing information among friends in OSNs. In view of characteristics of OSNs and the requirements of secure information flow, the oGBAC model also incorporates some ideas from the Attribute-Based Access Control (ABAC) to develop information flow based rules using relationship among attributes (such as tags, time and security levels) of objects and subjects in OSNs. Administration related rules and access related rules are designed for each access operation of group based OSNs' information sharing. The security of oGBAC model is analyzed using formal methods. To demonstrate the usability of the oGBAC model, we implement the model with the Comparative Attribute-Based Encryption (CCP-CABE), and analyze the security and efficiency of the implemented system to prove the effectiveness of the implemented system.

Natural Disaster Resilience Approach (NDRA) to Online Social Networks

Numerous disasters impact people around the world, including floods, cyclones, tsunamis and so on. Disaster resilience (recovery) is a realtime challenge. The research goal of the proposed Natural Disaster Resilience Approach (NDRA) is to fasten and automate disaster resilience within the affected region. Prioritization for saving lives must be provided to the region most affected. Currently almost all people use the Online Social Networks (OSNs) to share their daily activities. Actually only their friends in OSN can note the photos posted by OSN users in the disaster-affected region and in turn can support the friends in need. Our research goal is to conduct speedier and prioritized disaster resilience processes by directly addressing honest disaster help requests from images posted by OSN users. To achieve the above goal, we suggest an approach known as the Natural Disaster Resilience Approach (NDRA). The NDRA is a three-tiered framework: Tier-1: sybil(malicious) user avoidance engine: prevents fake OSN user profile creation; Tier-2: ‘D’- attributed image classifier: determines the originality of the images posted and Tier-3: sybil user prediction: uses an Advanced Sybil Node Prediction Algorithm (ASYNPA) to check the authenticity of the user and priorities for faster resilience requests. In NDRA we use Advogato dataset with 6541 users and 51,127 edges. Ultimately, the comparison is only made between the proposed ASYNPA tier-3 and the existing VoteTrust algorithm, and the graph is plotted against the False Positive (FP) rate, taking into account the precision and recall metrics. Around 99.84% of the expected sybils were confirmed in ASYNPA, which is 3.49% higher than VoteTrust.

Do I Deserve to Spend? Online Social Support and Spending Pleasure

Despite evidence of people posting their consumption experiences to online social networks to fulfill the needs of social support, an understanding of how online social support affects post-consumption spending behaviors remains elusive. This research aims to examine how online social support from online social network friends and the firm influence perceptions of self-deservingness and spending pleasure. Across two studies, this research provides evidence that social support gained through online social networks influences consumers’ spending pleasure through perceptions of their own deservingness. Notably, this study reveals that people obtain social support in online social networks from two sources: social networks friends and firms through receiving “Likes” and “Comments” on their post. This study also explores boundary conditions for when online social support is more effective on spending pleasure. The findings not only broaden the social support literature but also address the benefit to the service industry by understanding how social support can enhance spending pleasure.

Online social network use among parents of young childhood cancer survivors.

82 Background: Online social networks (OSNs) have emerged in the past decade as potentially powerful tools for health information sharing and health behavior change. According to a recent study by the Pew Research Center, 75% of all parents are active on OSNs. Given the high level of need for childhood cancer survivorship health education and preventive health behavior change, OSNs represent key resources for parents to engage with experts and peers over survivorship health. Methods: A cross-sectional survey of 114 parents of young and adolescent ( < 13 years) childhood cancer survivors was used to examine parent OSN use in specific platforms. Recruitment was conducted through online social support groups of two childhood cancer non-profits that focus on parent support. The novel online survey investigated OSN frequency of use, history of making new friends in OSNs, and whether OSNs were “good for sharing and receiving reliable and high quality information on diet, physical activity, and other healthy behaviors.” Results: Parents reported high levels of OSN use with 80% reporting daily use. Facebook was the most commonly used OSN by a wide margin. Seventy-eight percent of parents reported using it every day. The next most used OSN was Instagram (15%). Parents reported making new friends on Facebook (86%), followed by Instagram (22%) and Twitter (10%). Among parents who have used the respective OSNs, several were endorsed as being “good” or “extremely good” for sharing survivorship-related preventive health information. Facebook was endorsed by 76% of its users, Twitter and Pinterest by 59%, Google+ by 52%, and Instagram by 40%. None of the demographic variables collected predicted parent OSN use or perception. There was a strong association between parents using OSNs daily and their reporting having made new friends on OSNs (p < .000). Conclusions: Parents of young childhood cancer survivors recruited from online support groups report high levels of OSN use and making new friends through OSNs, and find OSNs potentially good sources for sharing and receiving survivorship-related health information. OSNs in general, and Facebook in particular, represent promising avenues for childhood cancer survivorship health information dissemination.

Predicting individual retweet behavior by user similarity: A multi-task learning approach

Users read microblogs and retweet the most “interesting” tweets to their friends in online social networks. Predicting retweet behavior is extremely challenging due to various reasons. First, the most of existing approaches primarily discuss a global retweet predicting model, with a goal of finding a uniform model that fits all users, but ignore individual behavior. And while social influence plays an important role in information diffusion, this fact has been largely ignored in conventional research. In this paper, we adopt a “microeconomics” approach to a model, and predict the individual retweet behavior. We study relationships between users by considering social similarity, which reflects how a particular retweeting action affects both the originator and the receiver of the retweet. To address the individual and social challenges, we analyze the effect of social similarity on retweet behavior based on a real dataset. Moreover, we cast our predicting problem as a multi-task learning problem. Combining the social and individual understanding, we then propose a novel model for predicting individual retweet behavior. We conduct extensive experiments on a Weibo (http://weibo.com, the largest microblogging service in China) dataset to validate the effectiveness of the proposed model. Our results demonstrate the superior performance of the proposed model, compared with several alternative classification methods.

SocialTube: P2P-Assisted Video Sharing in Online Social Networks

Video sharing has been an increasingly popular application in online social networks (OSNs). However, its sustainable development is severely hindered by the intrinsic limit of the client/server architecture deployed in current OSN video systems, which is not only costly in terms of server bandwidth and storage but also not scalable with the soaring amount of users and video content. The peer-assisted Video-on-Demand (VoD) technique, in which participating peers assist the server in delivering video content, has been proposed recently. Unfortunately, videos can only be disseminated through friends in OSNs. Therefore, current VoD works that explore clustering nodes with similar interests or close location for high performance are suboptimal, if not entirely inapplicable, in OSNs. Based on our long-term real-world measurement of over 1,000,000 users and 2,500 videos on Facebook, we propose SocialTube, a novel peer-assisted video sharing system that explores social relationship, interest similarity, and physical location between peers in OSNs. Specifically, SocialTube incorporates four algorithms: a social network (SN)-based P2P overlay construction algorithm, an SN-based chunk prefetching algorithm, chunk delivery, and scheduling algorithm, and a buffer management algorithm. Experimental results from a prototype on PlanetLab and an event-driven simulator show that SocialTube can improve the quality of user experience and system scalability over current P2P VoD techniques.

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 mutualevolving 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.

Publishing and sharing encrypted data with potential friends in online social networks

ABSTRACTMaking friends by publishing and sharing personal data in a special interest group has become popular in online social networks. Data security is a major concern, as digital content can be easily accessed from all over the Internet, and the online social network service provider is often for profit and semi‐trusted. The standard solution to data security is encryption, but sharing of encrypted data then becomes a challenging task. In this paper, employing attribute‐based encryption (ABE), we propose Masque+, a novel hierarchical and fine‐grained access control mechanism. On the basis of key policy ABE, the service provider manages users on the system level, but without being able to access their sensitive data. On the basis of ciphertext policy ABE, members of an interest group may customize their own access policies specifically. Masque+ features pragmatic functionalities like user revocation on the system level and the group level, respectively. We also build a prototype to validate the cryptographic algorithms involved in Masque+ and evaluate their performances. Real experimental data show that Masque+ incurs fairly reasonable costs even in cases where the access control policy is significantly complex. Copyright © 2013 John Wiley & Sons, Ltd.