Authors augment their texts using devices such as bold and italic typeface to signal important information to the reader. These typographical text signals are an example of a signal designed to have some affect on others. However, some signals emerge through the unplanned, indirect, and collective efforts of a group of individuals. Paths emerge in parks without having been designed by anyone. Objects accumulate wear patterns that signal how others have interacted with the object. Books open to important, well studied pages because the spine has worn, for example (Hill, Hollan, Wroblewski, & McCandless, 1992). Digital text and the large-scale collaboration made possible through the internet provide an opportunity to examine how unplanned, collaborative text signals could emerge. A software application was designed, called CoREAD, that enables readers to highlight sections of the text they deem important. In addition, CoREAD adds text signals to the text using font colour, based on the group's collective history and an aggregation function based on self-organising systems. The readers are potentially influenced by the text signals presented by CoREAD but also help to modify these same signals. Importantly, readers only interact with each other indirectly through the text. The design of CoREAD was greatly inspired by the previous work on history-enriched digital objects (Hill & Hollan, 1993) and at a more general level it can be viewed as an example of distributed cognition (Hollan, Hutchins, & Kirsh, 2000). / Forty undergraduate students read two texts on topics from psychology using CoREAD. Students were asked to read each text in order to write a summary of it. After each new student read the text, the text signals were changed to reflect the current group of students. As such, each student read the text with different text signals presented. / The data were analysed for each text to determine if the text signals that emerged were stable and valid representations of the semantic content of the text. As well, the students' summaries were analysed to determine if students who read the text after the text signals had stabilised produced better summaries. Three methods demonstrated that CoREAD was capable of generating stable typographical text signals. The high importance text signals also appeared to capture the semantic content of the texts. For both texts, a summary made of the high signals performed as well as a benchmark summary. The results did not suggest that the stable text signals assisted readers to produce better summaries, however. Readers may not respond to these collaborative text signals as they would to authorial text signals, which previous research has shown to be beneficial (Lorch, 1989). The CoREAD project has demonstrated that readers can produce stable and valid text signals through an unplanned, self-organising process.

<p>Serving more than one billion users around the world, today's online </p><p>social networks (OSNs) pervade our everyday life and change the way people </p><p>connect and communicate with each other. However, the open nature of </p><p>OSNs attracts a constant interest in attacking and exploiting them. </p><p>In particular, they are vulnerable to various attacks launched through </p><p>malicious accounts, including fake accounts and compromised real user </p><p>accounts. In those attacks, malicious accounts are used to send out </p><p>spam, spread malware, distort online voting, etc.</p><p>In this dissertation, we present practical systems that we have designed </p><p>and built to help OSNs effectively throttle malicious accounts. The overarching </p><p>contribution of this dissertation is the approaches that leverage the fundamental </p><p>weaknesses of attackers to defeat them. We have explored defense schemes along </p><p>two dimensions of an attacker's weaknesses: limited social relationships </p><p>and strict economic constraints.</p><p>The first part of this dissertation focuses on how to leverage social </p><p>relationship constraints to detect fake accounts. We present SybilRank, a novel </p><p>social-graph-based detection scheme that can scale up to OSNs with billions of </p><p>users. SybilRank is based on the observation that the social connections between </p><p>fake accounts and real users, called attack edges, are limited. It formulates </p><p>the detection as scalable user ranking according to the landing probability of </p><p>early-terminated random walks on the social graph. SybilRank generates an informative </p><p>user-ranked list with a substantial fraction of fake accounts at the bottom, </p><p>and bounds the number of fake accounts that are ranked higher than legitimate </p><p>users to O(log n) per attack edge, where n is the total number of users. We have </p><p>demonstrated the scalability of SybilRank via a prototype on Hadoop MapReduce, </p><p>and its effectiveness in the real world through a live deployment at Tuenti, </p><p>the largest OSN in Spain.</p><p>The second part of this dissertation focuses on how to exploit an attacker's </p><p>economic constraints to uncover malicious accounts. We present SynchroTrap, a system </p><p>that uncovers large groups of active malicious accounts, including both fake </p><p>accounts and compromised accounts, by detecting their loosely synchronized actions.</p><p>The design of SynchroTrap is based on the observation that malicious accounts usually </p><p>perform loosely synchronized actions to accomplish an attack mission, due to </p><p>limited budgets, specific mission goals, etc. SynchroTrap transforms the detection </p><p>into a scalable clustering algorithm. It uncovers large groups of accounts </p><p>that act similarly at around the same time for a sustained period of time. To </p><p>handle the enormous volume of user action data in large OSNs, we designed SynchroTrap</p><p>as an incremental processing system that processes small data chunks on a daily </p><p>basis but aggregates the computational results over the continuous data stream. </p><p>We implemented SynchroTrap on Hadoop and Giraph, and we deployed it on Facebook </p><p>and Instagram. This deployment has resulted in the unveiling of millions of malicious </p><p>accounts and thousands of large attack campaigns per month.</p> / Dissertation

The first-order properties like degree distribution of nodes and the clustering co-efficient have been the prime focus of research in the study of structural properties of networks. The presence of a power law in the degree distribution of nodes has been considered as an important structural characteristic of social and information networks. Higher-order structural properties such as edge embeddedness may also play a more important role in many on-line social networks but have not been studied before. In this research, we study the distribution of higher-order structural properties of a network, such as edge embeddedness, in complex network models and on-line social networks. We empirically study the embeddedness distribution of a variety of network models and theoretically prove that a recently-proposed network model, the random $k$-tree, has a power-law embedded distribution. We conduct extensive experiments on the embeddedness distribution in real-world networks and provide evidence on the correlation between embeddedeness and communication patterns among the members in an on-line social network. / Graduate

Thesis (Ph.D.)--Ohio University, August, 2008. / Title from PDF t.p. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 2008. / Typescript. Includes bibliographical references (leaves 22-25).

Thesis (Ph. D.)--Duke University, 2008.

Thesis (M.Ap.C.T. & M.)--Cleveland State University, 2008 / Abstract. Title from PDF t.p. (viewed on July 3, 2008). Includes bibliographical references (p. 92-99). Available online via the OhioLINK ETD Center. Also available in print.

Thesis (M.A.)--Northern Kentucky University, 2008. / Made available through ProQuest. Publication number: AAT 1450503. ProQuest document ID: 1490081771. Includes bibliographical references (p. 79-82)

Thesis (B.A.)--Haverford College, Dept. of Sociology, 2009. / Includes bibliographical references.

Thesis (M.A.I.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 85-94). Also available on the World Wide Web.