Portrait of a Privacy Invasion
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Abstract The popularity of online social networks has changed the way in which we share personal thoughts, political views, and pictures. Pictures have a particularly important role in the privacy of users, as they can convey substantial information (e.g., a person was attending an event, or has met with another person). Moreover, because of the nature of social networks, it has become increasingly difficult to control who has access to which content. Therefore, when a substantial amount of pictures are accessible to one party, there is a very serious potential for violations of the privacy of users. In this paper, we demonstrate a novel technique that, given a large corpus of pictures shared on a social network, automatically determines who is dating whom, with reasonable precision. More specifically, our approach combines facial recognition, spatial analysis, and machine learning techniques to determine pairs that are dating. To the best of our knowledge, this is the first privacy attack of this kind performed on social networks. We implemented our approach in a tool, called Creepic, and evaluated it on two real-world datasets. The results show that it is possible to automatically extract non-obvious, and nondisclosed, relationships between people represented in a group of pictures, even when the people involved are not directly part of a connected social clique.Keywords:
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Real-world groups are organizations or communities existed in the real world, such as the employees of a company, the students of a school, different from the virtual communities in social networks. The members of a real-world group may also appear in the social network and form into a virtual community. However, the community detection methods are not effective to detect the real-world groups because the members may lack interaction and sensitive attributes in the social network, so that the real-world groups appear to be hidden in the social network. This paper defines three kinds of real-world group models and defines sensitive attributes and sensitive relationships of users in real-world groups. We use random walk to detect memberships for real-world groups hidden in social network with no or little edges and sensitive attributes. We evaluate our model with a Facebook dataset. The experiments show that our model has an accuracy of 95%.
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Abstract We investigated the contribution of popularity, popularity prioritization, and gender to the explanation of bullying and defending behavior. Participants were 191 early adolescents (124 girls and 67 boys), aged from 10.9 to 13.6 years. Results revealed that adolescents high on popularity were more likely to bully others. Greater popularity prioritization was also associated with more bullying among boys with high levels, and girls with low levels, of popularity. In addition, popularity was positively related to defending among girls, but not boys. Lower popularity prioritization also contributed to greater defending overall. The implications of these findings for understanding bullying and defending are discussed.
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From Flickr to Facebook to Pinterest, pictures are increasingly becoming a core content type in social networks. But, how important is this visual content and how does it influence behavior in the network? In this paper we study the effects of visual, textual, and social factors on popularity in a large real-world network focused on fashion. We make use of state of the art computer vision techniques for clothing representation, as well as network and text information to predict post popularity in both in-network and out-of-network scenarios. Our experiments find significant statistical evidence that social factors dominate the in-network scenario, but that combinations of content and social factors can be helpful for predicting popularity outside of the network. This in depth study of image popularity in social networks suggests that social factors should be carefully considered for research involving social network photos.
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Groups with different interests exist within the working class, whose objective basis is the different connections of allocating resources. With the developing of market-oriented economy, the different groups within the working class will be more and more independent and strata will be more and more distinct. Managers that can allocate means of production, do their allocating duty and acquire relevant interests, belong to the social clique but workers that are managed, allocated by managers belong to the other social clique, who do labour directly and earn their wages.
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With the spreading of Marxism popularity in recent years,profound research has been done to the popularity of Marxism from different aspects and perspectives,including the popularity’s scientific implication,necessity,problems,past experiences,and the ways of promoting the popularity.The research is advantageous to the better development of Marxism popularity.
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Abstract With the development of the society, people increasingly frequent application in social network, the formation of social networking groups become important step in people’s life. How to analyze the evolution of social network group relations become very important. In this paper, an Floyd-Warshall evolution relations algorithm based on social networks key groups is proposed. Mainly includes four parts, initial construction of social network group relations, social big data acquisition, network group relations weighting, and group relations reconstruction. Experimental results show that, by analyzing group communication frequency, group membership relationship and the number of social group members, the evolution map of social network group relationship can be obtained, which can make a more intuitive analysis and expression of the development process of social network group and the evolution of social network group relationship.
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Currently, millions of individuals are sharing personal information and building social relations with others, through online social network sites. Recent research has shown that those personal information could compromise owners' privacy. In this work, we are interested in the privacy of online social network users with missing personal information. We study the problem of inferring those users' personal information via their social relations. We present an iterative algorithm, by combining a Bayesian label classification method and discriminative social relation choosing, for inferring personal information. Our experimental results reveal that personal information of most users in an online social network could be inferred through mere social relations with high accuracy.
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This paper deals with the various forms and changes of modern literature characteristics of popularity and furthermore the relationship between the popularity and marketing so that better understanding of the popularity of literature is achieved.
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The high education popularity is the inevitable requirement of development of economy and society.The essay discusses the theorical cause of higher education popularity and analyses the basic conditions for development of the higher education popularity and points that the higher education popularity is an inevitable alternative of the higher education of the new century.
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