Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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    Prediction, evolution and privacy in social and affiliation networks
    (2011) Zheleva, Elena; Getoor, Lise; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In the last few years, there has been a growing interest in studying online social and affiliation networks, leading to a new category of inference problems that consider the actor characteristics and their social environments. These problems have a variety of applications, from creating more effective marketing campaigns to designing better personalized services. Predictive statistical models allow learning hidden information automatically in these networks but also bring many privacy concerns. Three of the main challenges that I address in my thesis are understanding 1) how the complex observed and unobserved relationships among actors can help in building better behavior models, and in designing more accurate predictive algorithms, 2) what are the processes that drive the network growth and link formation, and 3) what are the implications of predictive algorithms to the privacy of users who share content online. The majority of previous work in prediction, evolution and privacy in online social networks has concentrated on the single-mode networks which form around user-user links, such as friendship and email communication. However, single-mode networks often co-exist with two-mode affiliation networks in which users are linked to other entities, such as social groups, online content and events. We study the interplay between these two types of networks and show that analyzing these higher-order interactions can reveal dependencies that are difficult to extract from the pair-wise interactions alone. In particular, we present our contributions to the challenging problems of collective classification, link prediction, network evolution, anonymization and preserving privacy in social and affiliation networks. We evaluate our models on real-world data sets from well-known online social networks, such as Flickr, Facebook, Dogster and LiveJournal.
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    Teacher Identification of Students for a Social-Emotional Intervention
    (2009) Sedlik, Samantha Lynn; Teglasi, Hedwig; Counseling and Personnel Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This study described how students received services for social-emotional issues in several schools where a social competence program was implemented. The study examined several variables including a) teacher referral practices in the context of a program designed as a prereferral intervention for these issues in elementary school-aged children; b) child characteristics; and c) group dynamics. Referring teachers completed pre and post-test behavior rating forms for 45 children (N=45) in the program. All students completed pre and post-test measures of listening comprehension and self-report measures of depression, anxiety, and anger. A case study of two children with different initial profiles highlights how initial child characteristics affect performance and progress in the group situation. The variability in child performance demonstrates the need for careful selection of participants when conducting group interventions in schools. Implications for prereferral interventions are discussed.
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    Keep it the same: Need for closure and the allure of homogeneous groups with impermeable boundaries
    (2007-07-17) Schultz, Jeremy Michael; Kruglanski, Arie W.; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The Need for (cognitive) Closure has been found to predict a "syndrome" of group-centric behaviors in numerous experiments (Kruglanski et al., 2006). This is theorized to be due to a strong desire for social reality, which groups can provide. The present research investigates the requisite conditions in which groups can fulfill this desire for a firm social reality, specifically group boundary impermeability and group homogeneity. It was found that Need for Closure predicted greater liking for the group only when the group was both homogeneous in composition and had impermeable boundaries, but not when only one of these conditions was met. These findings are explained using lay epistemic theory (Kruglanski, 1989).