Theses and Dissertations from UMD
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Item PUBLIC MOBILITY AND THE IMPACT ON SOCIAL NETWORKS: UNDERSTANDING THE SOCIETAL AND TRANSNATIONAL COMMUNICATION OF MIGRANT NETWORKS FROM A QUALITATIVE APPROACH(2023) Iannacone, Jeannette Isabelle; Sommerfeldt, Erich J; Communication; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In considering the realities of 21st century society, research cannot overlook how livelihoods are becoming increasingly defined by one’s (in)ability for and agency over movement, i.e. mobility, especially on the transnational scale. Simultaneously, the relational turn of public relations scholarship has emphasized a network perspective, examining how a set of relations among social actors—be it people, groups, or organizations—create systems that comprise, maintain, and/or disrupt society (Yang & Taylor, 2015). As such, public relations should be inclusive of the depth of multiple, rich, and mobile relationships in social networks that span national borders. Yet the development of the network perspective in public relations has not been without its limitations, notably the absence of public perspectives, actions, and realities—all of which impact the communicative interactions that produce their social networks. This research thereby incorporates a public perspective through insights from people who migrate to highlight an increasingly important dimension to public formation and relationship dynamics: mobility. In doing so, this dissertation takes an innovative qualitative approach to social network analysis (SNA), which integrates a visual network mapping exercise alongside qualitative interviews and ethnographic observations. Findings captured how the enactment and context of mobility impact migrant network dynamics across the world as well as their subsequent communication behaviors and relational expectations, particularly with U.S. civil society organizations (CSOs). They further depicted an organizational perspective that highlighted three dichotomies to how CSOs perceive and maintain their social networks, and showcased the role of mobility as an underlying context generating distinct actors, ties, and positioning. Findings lastly emphasized entanglements between social and other forms of capital as well as patterns in who is perceived as having versus needing capital.As such, this dissertation proposes the conceptualization of the mobile social network ecology, a concept that integrates social network analysis and the experiences of public mobility by accounting for distinct publics and organizations perceptions. It allows for public relations to better consider the impacts of the enactment and context of mobility on key public relationships, inclusive of the distinct publics of the modern world, the CSOs that seek to serve them, and their linkages to civil societies on a transnational scale. Additionally, in noting the significant ties between migrant publics and migrant-serving CSOs, this dissertation connects the exchanges of (social) capital within a mobile social network ecology to relational power dynamics and differentials, emphasizing their lived, embodied impact as well as introducing a new salient category: spatial capital. All together, these contributions advance public relations in reckoning with the transnational, globalized dimensions of the modern world, showcasing how public mobility shapes and complicates our fundamental societal connections and presenting unique takeaways for the field in scholarship and practice.Item TALKING ABOUT JUSTICE: PREDICTING ACTOR ENGAGEMENT ON SOCIAL MEDIA AFTER A GALVANIZING EVENT(2021) Glasgow, Kimberly Ann; Vitak, Jessica; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Social media contributes to discourse around and framing of major societal issues, and enables community formation, social change, and activism. It provides opportunities to engage in discourse, gain and share knowledge, and form ties with others around an issue, topic, or cause. This dissertation explores how justice, an important concept underlying social systems, is expressed in Twitter data in the context of high-salience, galvanizing local events, and leverages that information to predict whether newcomers to the issue will continue their digital engagement on the topic over time. It also attempts to quantify whether, and how much, a set of factors or dimensions previously associated with engagement in the physical realm contribute to digital engagement. These dimensions—identity, emotion, effort, and social embeddedness—are informed by prior work on social movements, digital activism, and related fields. Rather than rely on hashtags, this dissertation uses machine learning to detect justice-related Twitter activity. This advance in methods provides a richer understanding of discourse around a complex, multifaceted topic like justice. It allows deeper insight into the social media activity of newcomers to the justice community, and the networks they are embedded in. The approach is developed and applied first to Twitter data from Baltimore around the 2015 death of Freddie Gray from injuries sustained while in police custody, and the protests and riots that followed in Baltimore. To test for generalizability, the same approach is then applied to a second dataset, collected from Cleveland at the time of the death of Tamir Rice, who was shot and killed by police in 2014. Findings show that digital engagement in justice discourse on social media can be predicted, based on aspects of social embeddedness, emotion, and effort. To the degree that committed individuals are at the heart of social movements and efforts to spur social and civic change, and forming and being embedded in appropriate network structures is critical for channeling commitment into action and eventual success, this work contributes to greater understanding of these phenomena. Findings from this research could contribute to the design of technology to support civic engagement through social media platforms.Item Emergent behaviors in adaptive dynamical networks with applications to biological and social systems(2021) Alexander, Brandon Marc; Girvan, Michelle; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this thesis, we consider three network-based systems, focusing on emergent behaviors resulting from adaptive dynamical features. In our first investigation, we create a model for gene regulatory networks in which the network topology evolves over time to avoid instability of the regulatory dynamics. We consider and compare different rules of competitive edge addition that use topological and dynamical information from the network to determine how new network links are added. We find that aiming to keep connected components small is more effective at preventing widespread network failure than limiting the connections of genes with high sensitivity (i.e., potential for high variability across conditions). Finally, we compare our results to real data from several species and find a trend toward disassortativity over evolutionary time that is similar to our model for structure-based selection. In our second investigation, we introduce a bidirectional coupling between a phase synchronization model and a cascade model to produce our `sync-contagion' model. The sync-contagion model is well-suited to describe a system in which a contagious signal alerts individuals to realign their orientations, where `orientation' can be in the literal sense (such as a school of fish escaping the threat of a predator) or a more abstract sense (such as a `political orientation' that changes in response to a hot topic). We find that success in realigning the population towards some desired target orientation depends on the relative strengths of contagion spread and synchronization coupling. In our third and final investigation, we attempt to forecast the complex infection dynamics of the COVID-19 pandemic through a data-driven reservoir computing approach. We focus our attention on forecasting case numbers in the United States at the national and state levels. Despite producing adequate short-term predictions, we find that a simple reservoir computing approach does not perform significantly better than a linear extrapolation. The biggest challenge is the lack of data quantity normally required for machine learning success. We discuss methods to augment our limited data, such as through a `library-based' method or a hybrid modeling approach.Item Trends and Strategies of News on Social Media in the U.S.: A Multimethod Analysis(2019) Herd, Maria; Yaros, Ronald; Journalism; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There is growing interest in how social media and news interact, but much of that information is not widely available because news organizations pay third party analytics services for proprietary data. This study, however, employs a multimethod design to explore the issue. First, a quantitative analysis of audience data and social media trends is based on an aggregate of metrics (Parse.ly) from hundreds of news organizations to identify the most popular news categories on the top social networks (Facebook, Twitter, Pinterest, LinkedIn, Instagram, and Reddit). Second, qualitative interviews are conducted with social media strategists at four U.S. news organizations to capture emerging trends of best social media practices within newsrooms, including humanizing content, shifting coverage, training, encouraging subscriptions, third-party tools, and crowdsourcing.Item A BETTER NEIGHBORHOOD FOR HOUSING VOUCHER HOUSEHOLDS: OBSTACLES AND OPPORTUNITIES(2017) Jeon, Jae Sik; Dawkins, Casey J; Urban and Regional Planning and Design; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Since the 1970s, the emphasis of federal housing policy has shifted from place-based subsidies to tenant-based subsidies that are provided directly to low-income households for the purpose of renting in the private market. Although many hoped that the Housing Choice Voucher, a tenant-based housing assistance program, would be a new tool in the fight against concentrated poverty and its associated problems, housing voucher recipients still face obstacles when trying to secure housing in high-opportunity neighborhoods over the long-term. The growing body of evidence linking neighborhood conditions to household outcomes points to the need for a better understanding of how housing vouchers improve access to opportunities. While previous studies have explored neighborhood outcomes of housing voucher recipients, it still remains unclear what factors play a significant role in their residential location choices. My dissertation examines the constraints that housing voucher households face in neighborhood choices. Drawing upon data from the Moving to Opportunity experiment, it specifically analyzes trends in affordable housing inequality, estimates the effect of vehicle access on locational attainment, and explores social networks as a determinant of mobility behavior. The results of these analyses show that obstacles such as affordable housing inequality across the metropolitan area, strong social networks in the initial, poor neighborhood, and a lack of access to vehicles negatively affect the likelihood of moving to neighborhoods in which opportunities are expanded for low-income households. My findings shed light on the dynamics of residential mobility and neighborhood improvements for low-income households. The expansion of the Housing Choice Voucher program, supported by localized payment standard, connection to automobile subsidies, and extensive housing search services that provide information about the opportunities available in across all geographic units, may have a significant impact on poverty de-concentration and access to opportunity over time. These findings are also expected to bridge the gap between research and policy with regard to how housing voucher program could be improved in the context of the federal government’s charge to Affirmatively Further Fair Housing (AFFH).Item Mathematical Programming Models for Influence Maximization on Social Networks(2016) Zhang, Rui; Raghavan, Subramanian; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we apply mathematical programming techniques (i.e., integer programming and polyhedral combinatorics) to develop exact approaches for influence maximization on social networks. We study four combinatorial optimization problems that deal with maximizing influence at minimum cost over a social network. To our knowl- edge, all previous work to date involving influence maximization problems has focused on heuristics and approximation. We start with the following viral marketing problem that has attracted a significant amount of interest from the computer science literature. Given a social network, find a target set of customers to seed with a product. Then, a cascade will be caused by these initial adopters and other people start to adopt this product due to the influence they re- ceive from earlier adopters. The idea is to find the minimum cost that results in the entire network adopting the product. We first study a problem called the Weighted Target Set Selection (WTSS) Prob- lem. In the WTSS problem, the diffusion can take place over as many time periods as needed and a free product is given out to the individuals in the target set. Restricting the number of time periods that the diffusion takes place over to be one, we obtain a problem called the Positive Influence Dominating Set (PIDS) problem. Next, incorporating partial incentives, we consider a problem called the Least Cost Influence Problem (LCIP). The fourth problem studied is the One Time Period Least Cost Influence Problem (1TPLCIP) which is identical to the LCIP except that we restrict the number of time periods that the diffusion takes place over to be one. We apply a common research paradigm to each of these four problems. First, we work on special graphs: trees and cycles. Based on the insights we obtain from special graphs, we develop efficient methods for general graphs. On trees, first, we propose a polynomial time algorithm. More importantly, we present a tight and compact extended formulation. We also project the extended formulation onto the space of the natural vari- ables that gives the polytope on trees. Next, building upon the result for trees---we derive the polytope on cycles for the WTSS problem; as well as a polynomial time algorithm on cycles. This leads to our contribution on general graphs. For the WTSS problem and the LCIP, using the observation that the influence propagation network must be a directed acyclic graph (DAG), the strong formulation for trees can be embedded into a formulation on general graphs. We use this to design and implement a branch-and-cut approach for the WTSS problem and the LCIP. In our computational study, we are able to obtain high quality solutions for random graph instances with up to 10,000 nodes and 20,000 edges (40,000 arcs) within a reasonable amount of time.Item Transferring social capital from individual to team: An examination of moderators and relationships to innovative performance(2012) Edinger, Suzanne; Tesluk, Paul E; Business and Management: Management & Organization; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, I explore the relationships between individual social capital, team social capital, and team innovative performance. The association between personal and group social capital is underexplored (Burt, 2000; Kilduff & Krackhardt, 2008), and is important to investigate so that we may improve our knowledge of how social capital transfers from individuals to their teams in ways that promote team innovation. I hope to contribute to the literature on social capital in teams in three important ways. Within team-based settings with high innovation requirements, I first propose that the structural bridging social capital (i.e., ties outside the team) of team members is an important predictor of the team's structural bridging social capital. Second, transferring social capital from the individual to team level, I suggest that a team member's sharing of his/her bridging social capital resources is influenced by relational, cognitive, and task components, including group identification, dyadic trust, team member exchange, and shared vision. Finally, I investigate the role of transactive memory systems and bonding social capital (i.e., ties inside the team) in explaining the relationship between team structural bridging social capital and team innovative performance. Study participants were 263 members of 38 project teams in the merchandising displays division of a large paperboard and packaging manufacturer in the United States. I find that individual bridging social capital predicts team structural bridging social capital. Additionally, psychological identification with team, psychological identification with organization, team member exchange, and shared vision moderate the relationship between individual and team structural social capital. I conclude by discussing the implications of these findings for social capital and team innovative performance theory and practice.Item 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.Item Development, Technology Adoption, and Social Networks(2011) Vasilaky, Kathryn Nadine; Vasilaky, Kathryn Nadine; Agricultural and Resource Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Agriculture remains a key component of economic development, but the methodology for how development policies are determined has changed for developing countries. In the last decade, the focus of economic growth in developing countries has shifted from country-wide prescriptions to testable micro-development programs at the local level. As international development focuses in on local programs, social networks have been identified as a key component for their effective deployment. This dissertation analyzes the effects of a social network-based intervention. It contributes to the economics literature on identifying social network effects by implementing a randomized encouragement design to develop social capital, while simultaneously introducing a new method of development training. The program implemented here is comprised of two parts, and was conducted with female-headed households in rural Uganda, that were growing a relatively new cash crop, cotton. The first part conducted social network-based information games in 20 sample villages, in which each participant was trained in one aspect of cultivating cotton, and encouraged to attain a full set of knowledge on growing cotton through her assigned learning networks. They were presented with two different incentives schemes for accumulating information: competitive and team incentives. The second portion of the program paired the surveyed individuals at random with other game participants. These pairs were encouraged to develop team goals across the growing season and a time schedule for networking as well as update and share their learned information from the games on a regular basis. The estimated effects of the SNI, which comprise this dissertation, include both the effects from the information games and the effects of the mentored pairing; that is, the impact of acquiring one information point and one new link. I compare the effects of this program to a standard agricultural training program that was concurrently conducted during this research, in which extension agents taught the same information that was presented in the information games but with a traditional classroom-based teaching method. My games analysis shows that females learn more when presented with competitive incentives. The total number of learning points learned during competitive incentives first order stochastically dominates the total number of learning points learned during team incentives. However, for the dissemination of one specific information point, team incentives are better at ensuring that a unique information point reaches the entire group. Difference in difference estimates, controlling for the training program, show that the overall SNI program had significant effects on the average farmer, with diminishing returns for higher yielding farmers. I find that these average effects are comparable to the effects of the conventional training program, but at a fifth of the implementation cost. A closer examination shows that the SNI program has its most significant effect for farmers growing around the average output when the program was started in 2009 (100-200 kgs/acre), while the Training program has its greatest and most significant impact for those yielding above the average output in 2009. Therefore, the two programs are not necessarily substitutes in how they effect change. My research shows that a competitive incentive structure coupled with social network-based learning serves as an effective paradigm for improving outcomes for the poorest producers.Item Decisions under Uncertainty in Decentralized Online Markets: Empirical Studies of Peer-to-Peer Lending and Outsourcing(2010) Lin, Mingfeng; Viswanathan, Siva; Lucas, Hank; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recent developments in information technologies, especially Web 2.0 technologies, have radically transformed many markets through disintermediation and decentralization. Lower barriers of entry in these markets enable small firms and individuals to engage in transactions that were otherwise impossible. Yet, the issues of informational asymmetry that plague traditional markets still arise, only to be exacerbated by the "virtual" nature of these marketplaces. The three essays of my dissertation empirically examine how participants, many of whom are entrepreneurs, tackle the issue of asymmetric information to derive benefits from trade in two different contexts. In Essay 1, I investigate the role of online social networks in mitigating information asymmetry in an online peer-to-peer lending market, and find that the relational dimensions of these networks are especially effective for this purpose. In Essay 2, I exploit a natural experiment in the same marketplace to study the effect of shared geographical ties on investor decisions, and find that "home bias" is not only robust but also has an interesting interaction pattern with rational decision criteria. In Essay 3, I study how the emergence of new contract forms, enabled by new monitoring technologies, changes the effectiveness of traditional signals that affect a buyers' choice of sellers in online outsourcing. Using a matched-sample approach, I show that the effectiveness of online ratings and certifications differs under pay-for-time contracts versus pay-for-deliverable contracts. In all, the three essays of my dissertation present new empirical evidence of how agents leverage various network ties, signals and incentives to facilitate transactions in decentralized online markets, form transactional ties, and reap the benefits enabled by the transformative power of information technologies.