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The increasing popularity of Online Social Networks (OSNs), its complex and rapidly growing structure, its potential impact on business, politics, healthcare and society has led to a great deal of interest in the development of efficient influential nodes identification algorithms. Different kind of business organizations are trying to use the social networking sites to reach the target audience quickly and efficiently in terms of cost and time. The influential nodes can play an important role in it. In this paper the features of social network, models of social networks and different models or approaches used for identifying influential nodes in online social networks have been studied.

The author analyzes online social network as a social institute of a virtual type where individuals can present themselves to others by means of special practices. At the same time he indicates that these practices are similar to real ones by their relative equality in particular space and time due to reflexivity of actors. There are three major functional regulators of users behavior in online social network: 1) quantitative restrictions on self presentation determined by information formats; 2) qualitative matter of self presentation conditioned on norms of Russian and International legislations; 3) rituals of virtual interaction. The study shows what role VK-user profile plays in modern human interactions and how it affects communication practices. Case-study analysis of the standard form of VK-user profile allowed to learn the features of communication tools of the social network that on the whole are used for exchanging self-presentation data between its users. The author particularly focuses on instrumental capabilities of a section of VK-user profile form called "Personal views" that includes closed questions about existential and social values. A person can partially present him/her self in the network by selecting a short templated response to each closed question from a narrow range of alternatives. The study also considers the functions of authorized pages of celebrities who are some kind of "significant others" to their multiple admirers.

"Following the introduction of Myspace, Linkedin and Facebook, social networks have become part of people's everyday life. New online social networks appear almost daily developed to attract the attention of Internet users. However, design and implementation standards and guidelines are often sacrificed for innovation resulting in unusable social networking platforms destined to be doomed. Within this context, the present book recommends a design and implementation methodology which will allow future social networking platform designers and developers to work in a scientifically systematic and sound manner to reach their goal. The journey begins with the identification of the pursued objective of the social network and the potential participants. The presentation of various methods and techniques to collect the requirements of the intended user group of the social networking platform follows. Best practices, guidelines and standards that will facilitate the conceptual and physical design of the platform are portrayed. An analysis of advantages and limitations for existing implementation platforms and potential implementation techniques is subsequently offered to facilitate the selection of the most appropriate tool for the implementation of the online social networking website. Various representative visualization techniques are examined in order to enhance the visual representation of the social network participants. Various usability evaluation techniques are then presented to aid in the assessment of usability and improvement of the actual user experience. Finally, future trends in the design and use of social networks are presented."--Publisher's website.

Online social networks (OSN) are becoming more important in people's daily life, however, all popular OSNs are centralized, and this raises a series of security, privacy and management issues. A decentralized architecture based on blockchain technology provides the ability to solve above issues. In this paper, an OSN service is developed based on blockchain technology in order to make it operate decentralized. Large volume of data normally required low-security requirements can be stored in Interplanetary Filesystem (IPFS) to make data decentralized. A decentralized autonomous organization is developed for user autonomy, users can self-manage the OSN in a democratic way.

With billions of users, Online Social Networks(OSNs) are amongst the largest scale communication applications on the Internet. OSNs enable users to easily access news from local and worldwide, as well as share information publicly and interact with friends. On the negative side, OSNs are also abused by spammers to distribute ads or malicious information, such as scams, fraud, and even manipulate public political opinions. Having achieved significant commercial success with large amount of user information, OSNs do treat the security and privacy of their users seriously and provide several mechanisms to reinforce their account security and information privacy. However, the efficacy of those measures is either not thoroughly validated or in need to be improved. In sight of cyber criminals and potential privacy threats on OSNs, we focus on the evaluations and improvements of OSN user privacy configurations, account security protection mechanisms, and trending topic security in this dissertation. We first examine the effectiveness of OSN privacy settings on protecting user privacy. Given each privacy configuration, we propose a corresponding scheme to reveal the target user's basic profile and connection information starting from some leaked connections on the user's homepage. Based on the dataset we collected on Facebook, we calculate the privacy exposure in each privacy setting type and measure the accuracy of our privacy inference schemes with different amount of public information. The evaluation results show that (1) a user's private basic profile can be inferred with high accuracy and (2) connections can be revealed in a significant portion based on even a small number of directly leaked connections. Secondly, we propose a behavioral-profile-based method to detect OSN user account compromisation in a timely manner. Specifically, we propose eight behavioral features to portray a user's social behavior. A user's statistical distributions of those feature values comprise its behavioral profile. Based on the sample data we collected from Facebook, we observe that each user's activities are highly likely to conform to its behavioral profile while two different user's profile tend to diverge from each other, which can be employed for compromisation detection. The evaluation result shows that the more complete and accurate a user's behavioral profile can be built the more accurately compromisation can be detected. Finally, we investigate the manipulation of OSN trending topics. Based on the dataset we collected from Twitter, we manifest the manipulation of trending and a suspect spamming infrastructure. We then measure how accurately the five factors (popularity, coverage, transmission, potential coverage, and reputation) can predict trending using an SVM classifier. We further study the interaction patterns between authenticated accounts and malicious accounts in trending. at last we demonstrate the threats of compromised accounts and sybil accounts to trending through simulation and discuss countermeasures against trending manipulation.

More and more researchers focus on the role of social networks in election campaigns. This article represents a case study of the parliamentary elections from 2015 in Spain and 2016 in Romania, with the aim of comparing the two online political campaigns. We describe how both parties in Romania and Spain used Facebook during the last parliamentary elections, in order to see how the political parties communicate and the online reactions generated by their messages. With the help of content and statistical analysis we take a closer look the messages published in the Facebook profiles of candidates and political parties during the general elections. The results indicate that, during parliamentary elections, unlike the presidential ones, the voters' attention is not directed to a candidate, but to a group of candidates. As a result, the communication strategy is different, focusing on increasing the notoriety of the candidates. The low interest in parties and parliamentary elections leads to using social networks mainly for disseminating information about the candidates and less as tool for mobilizing voters.

Online Social Networks (OSNs) are integrated into business, entertainment, politics, and education; they are integrated into nearly every facet of our everyday lives. They have played essential roles in milestones for humanity, such as the social revolutions in certain countries, to more day-to-day activities, such as streaming entertaining or educational materials. Not surprisingly, social networks are the subject of study, not only for computer scientists, but also for economists, sociologists, political scientists, and psychologists, among others. In this dissertation, we build a model that is used to classify content on the OSNs of Reddit, 4chan, Flickr, and YouTube according the types of lifespan their content have and the popularity tiers that the content reaches. The proposed model is evaluated using 10-fold cross-validation, using data mining techniques of Sequential Minimal Optimization (SMO), which is a support vector machine algorithm, Decision Table, Naïve Bayes, and Random Forest. The run times and accuracies are compared across OSNs, models, and data mining algorithms. The peak/death category of Reddit content can be classified with 64% accuracy. The peak/death category of 4Chan content can be classified with 76% accuracy. The peak/death category of Flickr content can classified with 65% accuracy. We also used 10-fold cross-validation to measure the accuracy in which the popularity tier of content can be classified. The popularity tier of content on Reddit can be classified with 84% accuracy. The popularity tier of content on 4chan can be classified with 70% accuracy. The popularity tier of content on Flickr can be classified with 66% accuracy. The popularity tier of content on YouTube can be classified with only 48% accuracy. Our experiments compared the runtimes and accuracy of SMO, Naïve Bayes, Decision Table, and Random Forest to classify the lifespan of content on Reddit, 4chan, and Flickr as well as classify the popularity tier of content on Reddit, 4chan, Flickr, and YouTube. The ...