Elena Zheleva

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In order to address privacy concerns, many social media websites allow users to hide their personal profiles from the public. In this work, we show how an adversary can exploit an online social network with a mixture of public and private user profiles to predict the private attributes of users. We map this problem to a relational classification problem and(More)
This paper addresses the problem of making text mining results more comprehensible to humanities scholars, journalists, intelligence analysts, and other researchers, in order to support the analysis of text collections. Our system, FeatureLens<sup>1</sup>, visualizes a text collection at several levels of granularity and enables users to explore interesting(More)
Spam is a growing problem; it interferes with valid email and burdens both email users and service providers. In this work, we propose a reactive spam-filtering system based on reporter reputation for use in conjunction with existing spam-filtering techniques. The system has a trust-maintenance component for users, based on their spam-reporting behavior.(More)
This synthesis lecture provides a survey of work on privacy in online social networks (OSNs). This work encompasses concerns of users as well as service providers and third parties. Our goal is to approach such concerns from a computer-science perspective, and building upon existing work on privacy, security, statistical modeling and databases to provide an(More)
User experience in social media involves rich interactions with the media content and other participants in the community. In order to support such communities, it is important to understand the factors that drive the users' engagement. In this paper we show how to define statistical models of different complexity to describe patterns of song listening in(More)
Social networks can capture a variety of relationships among the participants. Two of the most commonly studied are friendship and family, or kinship, ties. Most existing work studies these networks in isolation. Here, we study how these networks can be overlaid. We study the predictive power of overlaying friendship and family ties on a trio of interesting(More)
In order to address privacy concerns, many social media websites allow users to hide their personal profiles from the public. In this work, we show how an adversary can exploit a social network with a mixture of public and private user profiles to predict the private attributes of users. We map this problem to a relational classification problem and we(More)
Social network sites are extensively use for communication by social user. This communication among the sites produces an immense amount of data within the networks. The data that are associated with these sites hold essential information about users. To reveal the privacy of this essential information from unauthorized access is a critical issue in social(More)