Extraction and Analysis of Facebook Friendship Relations

Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms

Mitigating Colluding Attacks in Online Social Networks and Crowdsourcing Platforms

Online Social Networks (OSNs) have created new ways for people to communicate, and for companies to engage their customers -- with these new avenues for communication come new vulnerabilities that can be exploited by attackers. This dissertation aims to investigate two attack models: Identity Clone Attacks (ICA) and Reconnaissance Attacks (RA). During an ICA, attackers impersonate users in a network and attempt to infiltrate social circles and extract confidential information. In an RA, attackers gather information on a target\u27s resources, employees, and relationships with other entities over public venues such as OSNs and company websites. This was made easier for the RA to be efficient because well-known social networks, such as Facebook, have a policy to force people to use their real identities for their accounts. The goal of our research is to provide mechanisms to defend against colluding attackers in the presence of ICA and RA collusion attacks. In this work, we consider a scenario not addressed by previous works, wherein multiple attackers collude against the network, and propose defense mechanisms for such an attack. We take into account the asymmetric nature of social networks and include the case where colluders could add or modify some attributes of their clones. We also consider the case where attackers send few friend requests to uncover their targets. To detect fake reviews and uncovering colluders in crowdsourcing, we propose a semantic similarity measurement between reviews and a community detection algorithm to overcome the non-adversarial attack. ICA in a colluding attack may become stronger and more sophisticated than in a single attack. We introduce a token-based comparison and a friend list structure-matching approach, resulting in stronger identifiers even in the presence of attackers who could add or modify some attributes on the clone. We also propose a stronger RA collusion mechanism in which colluders build their own legitimacy by considering asymmetric relationships among users and, while having partial information of the networks, avoid recreating social circles around their targets. Finally, we propose a defense mechanism against colluding RA which uses the weakest person (e.g., the potential victim willing to accept friend requests) to reach their target

Geo Data Science for Tourism

This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.

Standards as interdependent artifacts : the case of the Internet

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references.This thesis has explored a new idea: viewing standards as interdependent artifacts and studying them with network analysis tools. Using the set of Internet standards as an example, the research of this thesis includes the citation network, the author affiliation network, and the co-author network of the Internet standards over the period of 1989 to 2004. The major network analysis tools used include cohesive subgroup decomposition (the algorithm by Newman and Girvan is used), regular equivalence class decomposition (the REGE algorithm and the method developed in this thesis is used), nodal prestige and acquaintance (both calculated from Kleinberg's technique), and some social network analysis tools. Qualitative analyses of the historical and technical context of the standards as well as statistical analyses of various kinds are also used in this research. A major finding of this thesis is that for the understanding of the Internet, it is beneficial to consider its standards as interdependent artifacts. Because the basic mission of the Internet (i.e. to be an interoperable system that enables various services and applications) is enabled, not by one or a few, but by a great number of standards developed upon each other, to study the standards only as stand-alone specifications cannot really produce meaningful understandings about a workable system. Therefore, the general approaches and methodologies introduced in this thesis which we label a systems approach is a necessary addition to the existing approaches. A key finding of this thesis is that the citation network of the Internet standards can be decomposed into functionally coherent subgroups by using the Newman-Girvan algorithm.(cont.) This result shows that the (normative) citations among the standards can meaningfully be used to help us better manage and monitor the standards system. The results in this thesis indicate that organizing the developing efforts of the Internet standards into (now) 121 Working Groups was done in a manner reasonably consistent with achieving a modular (and thus more evolvable) standards system. A second decomposition of the standards network was achieved by employing the REGE algorithm together with a new method developed in this thesis (see the Appendix) for identifying regular equivalence classes. Five meaningful subgroups of the Internet standards were identified, and each of them occupies a specific position and plays a specific role in the network. The five positions are reflected in the names we have assigned to them: the Foundations, the Established, the Transients, the Newcomers, and the Stand-alones. The life cycle among these positions was uncovered and is one of the insights that the systems approach on this standard system gives relative to the evolution of the overall standards system. Another insight concerning evolution of the standard system is the development of a predictive model for promotion of standards to a new status (i.e. Proposed, Draft and Internet Standards as the three ascending statuses). This model also has practical potential to managers of standards setting organizations and to firms (and individuals) interested in efficiently participating in standards setting processes. The model prediction is based on assessing the implicit social influence of the standards (based upon the social network metric, betweenness centrality, of the standards' authors) and the apparent importance of the standard to the network (based upon calculating the standard's prestige from the citation network).(cont.) A deeper understanding of the factors that go into this model was also developed through the analysis of the factors that can predict increased prestige over time for a standard. The overall systems approach and the tools developed and demonstrated in this thesis for the study of the Internet standards can be applied to other standards systems. Application (and extension) to the World Wide Web, electric power system, mobile communication, and others would we believe lead to important improvements in our practical and scholarly understanding of these systems.by Mo-Han Hsieh.Ph.D

Social Space and Social Media: Analyzing Urban Space with Big Data

This dissertation focuses on the key role that big data can play in minimizing the perceived disconnect between social theory and quantitative methods in the discipline of geography. It takes as its starting point the geographic concept of space, which is conceptualized very differently in social theory versus quantitative methodology. Contrary to this disparity, an examination of the disciplinary history reveals a number of historic precedents and potential pathways for a rapprochement, especially when combined with some of the new possibilities of big data. This dissertation also proposes solutions to two common barriers to the adoption of big data in the social sciences: accessing and collecting such data and, subsequently, meaningful analysis. These methods and the theoretical foundation are combined in three case studies that show the successful integration of a quantitative research methodology with social theories on space. The case studies demonstrate how such an approach can create new and alternative understandings of urban space. In doing so it answers three specific research questions: (1) How can big data facilitate the integration of social theory on space with quantitative research methodology? (2) What are the practical challenges and solutions to moving “beyond the geotag” when utilizing big data in geographical research? (3) How can the quantitative analysis of big data provide new and useful insight in the complex character of social space? More specifically, what insights does such an analysis of relational social space provide about urban mobility and cognitive neighborhoods