This dissertation analyzes the ways that race, class, and gender intersect in specific spatial contexts to shape access to opportunity and influence families' neighborhood decisions and experiences. I add to existing research by examining the initial processes of adjusting to new neighborhoods, focusing especially on the components of neighborhood transitions that are significant for low-income women and their children. I use in-depth qualitative interview data that was collected by Northwestern University's Institute for Policy Research between 2002 and 2005 with a randomly chosen sample of adults and youth in 91 families who participated in the Gautreaux Two housing mobility program in Chicago. This analysis assesses the various factors that influenced the range of program outcomes in order to understand the social processes involved. The results of this analysis show that respondents had complicated perspectives about moving out of public housing. Most respondents were eager to move out of their baseline neighborhoods, especially for the sake of their children. However, many also cited things they missed about the neighborhood once they moved. Many faced severe obstacles in locating an eligible unit. Some of these obstacles related to the poor implementation of the Gautreaux Two program, as well as to the tight rental market in Chicago at the time. After moving through the program, many families experienced hassles with landlords, substandard unit quality, distance from kin and support networks, and difficulty in creating new social ties in placement neighborhoods resulting in social isolation and transportation and financial difficulties. Other respondents had supportive relationships with landlords, good quality units, were able to maintain ties with kin, and developed relationships with new neighbors. Participants generally valued the racial diversity of their Gautreaux neighborhoods, and many emphasized the importance of having their children live in racially diverse areas. While some respondents' children faced discrimination in their new neighborhoods and schools, this was not the primary impetus for making subsequent mobility decisions. Policy implications include the need for further pre-move housing counseling for families in mobility programs, as well as continued program assistance to build and maintain strong social networks and connections to resources. / Sociology

The 900 day German blockade of Leningrad fostered an environment in which social relationships, which were pruned and altered during the 1930s, were reinvigorated and reinvented by Leningraders. By the outbreak of the war in the summer of 1941, Stalinist social engineering policies had eroded previously normalized social connections and networks. At the height of the Terror, it became beneficial and advantageous for Soviet citizens to cut off many of their social relationships that had been built up over years. The family became the site of the primary emphasis of social interaction. The strengthening of the family system under Stalin created family units that were remarkably elastic and durable. This familial elasticity allowed Leningraders to reknit social relationships during the siege which became primary as food became central to survival. Without intense monitoring and oversight from the state, Leningraders were forced to rekindle social ties and relationships to survive. / History

We introduce a spatial cost topology in the network formation model analyzed by Jackson and Wolinsky, <i>Journal of Economic Theory </i><b>71 </b> (1996), 44--74. This cost topology might represent geographical, social, or individual differences. It describes variable costs of establishing social network connections. Participants form links based on a cost-benefit analysis. We examine the pairwise stable networks within this spatial environment. Incentives vary enough to show a rich pattern of emerging behavior. We also investigate the subgame perfect implementation of pairwise stable and efficient networks. We construct a multistage extensive form game that describes the formation of links in our spatial environment. Finally, we identify the conditions under which the subgame perfect Nash equilibria of these network formation games are stable. We analyze the dynamic implications of learning in a large population coordination game where both the actions of the players and the communication network evolve over time. Cost considerations of social interaction are incorporated by considering a circular model with endogenous neighborhoods, meaning that the locations of the players are fixed but players can create their own communication network.The dynamic process describing medium-run behavior is shown to converge to an absorbing state, which may be characterized by coexistence of conventions. In the long run, when mistake probabilities are small but nonvanishing, coexistence of conventions is no longer sustainable as the risk-dominant convention becomes the unique stochastically stable state. We create and investigate a system that is capable of observing the accumulation of social capital and the effect of social capital accumulation on behavior of individually rational players. In the first model, we develop a restricted system to show that social capital forms and is maintained at a steady state level. The resulting network is the chain. The second model uses a congestion function in conjunction with social capital to show a network emerge that contains links that costlier than those in the chain network. / Ph. D.

In this work, we propose a PRivAcy-Diversity-Aware Team Formation framework, namely PRADA-TF, that can be deployed based on the trust relationships between users in online social networks (OSNs). Our proposed PRADA-TF is mainly designed to reflect team members' domain expertise and privacy preserving preferences when a task requires a wide range of diverse domain expertise for its successful completion. The proposed PRADA-TF aims to form a team for maximizing its productivity based on members' characteristics in their diversity, privacy preserving, and information sharing. We leveraged a game theory called Mechanism Design in order for a mechanism designer as a team leader to select team members that can maximize the team's social welfare, which is the sum of all team members' utilities considering team productivity, members' privacy preserving, and potential privacy loss caused by information sharing. To screen a set of candidate teams in the OSN, we built an expert social network based on real co-authorship datasets (i.e., Netscience) with 1,590 scientists, used the semi-synthetic datasets to construct a trust network based on a belief model called Subjective Logic, and identified trustworthy users as candidate team members. Via our extensive simulation experiments, we compared the seven different TF schemes, including our proposed and existing TF algorithms, and analyzed the key factors that can significantly impact the expected and actual social welfare, expected and actual potential privacy leakout, and team diversity of a selected team. / Master of Science / In this work, we propose a PRivAcy-Diversity-Aware Team Formation framework, namely PRADA-TF, that can be deployed based on the trust relationships between users in online social networks (OSNs). Our proposed PRADA-TF is mainly designed to reflect team members' domain expertise and privacy preserving preferences when a task requires a wide range of diverse domain expertise for its successful completion. The proposed PRADA-TF aims to form a team based on members' characteristics in their diversity, privacy preserving, and information sharing so as to maximize the performance of the team. We leveraged a game theory called Mechanism Design in order for a mechanism designer as a team leader to select team members that can maximize the team's social welfare, which is the sum of all team members' utilities considering team productivity, members' privacy preserving, and potential privacy loss caused by information sharing. To screen a set of candidate teams in the OSN, we built an expert social network based on real co-authorship datasets with 1,590 scientists, used the semi-synthetic datasets to construct a trust network representing the trust relationship between the users in OSNs, and identified trustworthy users as candidate team members. Via our extensive simulation experiments, we compared the seven different team formation (TF) schemes, including our proposed and existing TF algorithms, and analyzed the key factors that can significantly impact the expected and actual social welfare, expected and actual potential privacy leakout, and team diversity of a selected team.

Online social networks have become a staging ground for many modern movements, with the Arab Spring being the most prominent example. In an effort to understand and predict those movements, social media can be regarded as a valuable social sensor for disclosing underlying behaviors and patterns. To fully understand mass movement information propagation patterns in social networks, several problems need to be considered and addressed. Specifically, modeling mass movements that incorporate multiple spaces, a dynamic network structure, and misinformation propagation, can be exceptionally useful in understanding information propagation in social media. This dissertation explores four research problems underlying efforts to identify and track the adoption of mass movements in social media. First, how do mass movements become mobilized on Twitter, especially in a specific geographic area? Second, can we detect protest activity in social networks by observing group anomalies in graph? Third, how can we distinguish real movements from rumors or misinformation campaigns? and fourth, how can we infer the indicators of a specific type of protest, say climate related protest? A fundamental objective of this research has been to conduct a comprehensive study of how mass movement adoption functions in social networks. For example, it may cross multiple spaces, evolve with dynamic network structures, or consist of swift outbreaks or long term slowly evolving transmissions. In many cases, it may also be mixed with misinformation campaigns, either deliberate or in the form of rumors. Each of those issues requires the development of new mathematical models and algorithmic approaches such as those explored here. This work aims to facilitate advances in information propagation, group anomaly detection and misinformation distinction and, ultimately, help improve our understanding of mass movements and their adoption in social networks. / Ph. D.

yes / This paper provides an overview of the literature on AIDS activism, stigma, and violence. The literature on AIDS activism, stigma and violence discussed suggests that the physical, emotional and social violence that AIDS as a disease, and stigma as a social construct tied to that disease, can be turned into an empowering experience that joins HIV positive people in productive and constructive networks, that this empowerment fundamentally changes one¿s identity, and that such disease-based identities are reshaping notions of citizenship around the globe. This hypothesis is built, however, on theory and on experiences in a) richer countries with a completely different epidemiology than that of sub-Saharan Africa, b) a highly politicised and activist country such as South Africa, and on c) initial ethnographic evidence from West African countries. Although this seems enough evidence to tentatively observe a trend, we need far more evidence from diverse contexts if this transformative potential is to be explored to the full. The paper concludes by drawing out a research agenda.

With the growing spread of spatial data, exploratory analysis has gained a considerable amount of attention. Particularly in the fields of Information Retrieval and Data Mining, the integration of data points helps uncover interesting patterns not always visible to the naked eye. Social networks often link entities that share places and activities; marketing tools target users based on behavior and preferences; and medical technology combines symptoms to categorize diseases. Many of the current approaches in this field of research depend on semantic analysis, which is good for inferencing and decision making. From a functional point of view, objects can be investigated from a spatial and temporal perspectives. The former attempts to verify how proximity makes the objects related; the latter adds a measure of coherence by enforcing time ordering. This type of spatio-temporal reasoning examines several aspects of semantic analysis and their characteristics: shared relationships among objects, matches versus mismatches of values, distances among parents and children, and bruteforce comparison of attributes. Most of these approaches suffer from the pitfalls of disparate data, often missing true relationships, failing to deal with inexact vocabularies, ignoring missing values, and poorly handling multiple attributes. In addition, the vast majority does not consider the spatio-temporal aspects of the data. This research studies semantic techniques of data analysis in spatial contexts. The proposed solutions represent different methods on how to relate spatial entities or sequences of entities. They are able to identify relationships that are not explicitly written down. Major contributions of this research include (1) a framework that computes a numerical entity similarity, denoted a semantic footprint, composed of spatial, dimensional, and ontological facets; (2) a semantic approach that translates categorical data into a numerical score, which permits ranking and ordering; (3) an extensive study of GML as a representative spatial structure of how semantic analysis methods are influenced by its approaches to storage, querying, and parsing; (4) a method to find spatial regions of high entity density based on a clustering coefficient; (5) a ranking strategy based on connectivity strength which differentiates important relationships from less relevant ones; (6) a distance measure between entity sequences that quantifies the most related streams of information; (7) three distance-based measures (one probabilistic, one based on spatial influence, and one that is spatiological) that quantifies the interactions among entities and events; (8) a spatio-temporal method to compute the coherence of a data sequence. / Ph. D.

Leadership programs serve as a mechanism to develop the leadership capacity of individuals, groups, and organizations. Although considerable time and resources have been devoted to understanding the outcomes of leadership development, little time and effort has been dedicated to understanding the developmental approaches that influence the emergence of these outcomes. The purpose of this study was to explore and untangle the relationships between common leadership development approaches, networking ability, and social capital outcomes. A sample of graduates from 15 agricultural-based leadership development programs, and a two-phase, convergent parallel mixed methods (QUAN + QUAL) design, were used to assess the relationships between the aforementioned constructs. Specifically, this research explored the influence of common leadership development approaches – conceptual understanding, feedback, personal growth, and skill building – on networking ability and the following dimensions of social capital: (a) groups and networks, (b) trust and solidarity, and (c) cooperation and political action. Quantitative data were collected using a cross-sectional, web-based survey (n = 231), and qualitative data were collected using semi-structured interviews (n =11). Equal priority was given to the quantitative and qualitative data, which was collected concurrently, analyzed independently, and mixed at the conclusions and metainference stage. The findings indicate that participation in an agricultural leadership program influences the social capital capacity of graduates by providing opportunities that facilitate the emergence of new, appropriable social networks. Through a variety of learning activities and shared experiences, participants diversify their social network and develop strong network connections. These connections, and the embedded social capital (i.e., relational) resources, are being accessed frequently for advice, information, and support. As reported by program graduates, personal growth and skill building were identified as the most influential approaches for developing leadership capacity. These approaches, which are characterized by collaborative, group learning, also had the strongest relationships with networking ability. Networking ability should be specifically targeted and included in leadership development curricula because it can influence cooperation and political action, which have been described as the highest levels of social capital. Informal learning also plays an important role in the development of trust, strong bonds, and solidarity among program participants. / Ph. D.

Natural disasters exert a profound impact on the world population. In 2012, natural disasters affected 106 million people, forcing over 31.7 million people to leave their homes. Climate change has intensified natural disasters, resulting in more catastrophic events and making extreme weather more difficult to predict. Understanding and predicting human movements plays a critical role in disaster evacuation, response and relief. Researchers have developed different methodologies and applied several models to study human mobility patterns, including random walks, Lévy flight, and Brownian walks. However, the extent to which these models may apply to perturbed human mobility patterns during disasters and the associated implications for improving disaster evacuation, response and relief efforts is lacking. My PhD research aims to address the limitation in human mobility research and gain a ground truth understanding of human mobility patterns under the influence of natural disasters. The research contains three interdependent projects. In the first project, I developed a novel data collecting system. The system can be used to collect large scale data of human mobility from large online social networking platforms. By analyzing both the general characteristics of the collected data and conducting a case study in NYC, I confirmed that the data collecting system is a viable venue to collect empirical data for human mobility research. My second project examined human mobility patterns in NYC under the influence of Hurricane Sandy. Using the data collecting system developed in the first project, I collected 12 days of human mobility data from NYC. The data set contains movements during and several days after the strike of Hurricane Sandy. The results showed that human mobility was strongly perturbed by Hurricane Sandy, but meanwhile inherent resilience was observed in human movements. In the third project, I extended my research to fifteen additional natural disasters from five categories. Using over 3.5 million data entries of human movement, I found that while human mobility still followed the Lévy flight model during these disaster events, extremely powerful natural disasters could break the correlation between human mobility in steady states and perturbation states and thus destroy the inherent resilience in human mobility. The overall findings have significant implications in improving understanding and predicting human mobility under the influence of natural disasters and extreme events. / Ph. D.

Yes / Social media comprises communication websites that facilitate relationship forming between users from diverse backgrounds, resulting in a rich social structure. User generated content encourages inquiry and decision-making. Given the relevance of social media to various stakeholders, it has received significant attention from researchers of various fields, including information systems. There exists no comprehensive review that integrates and synthesises the findings of literature on social media. This study discusses the findings of 132 papers (in selected IS journals) on social media and social networking published between 1997 and 2017. Most papers reviewed here examine the behavioural side of social media, investigate the aspect of reviews and recommendations, and study its integration for organizational purposes. Furthermore, many studies have investigated the viability of online communities/social media as a marketing medium, while others have explored various aspects of social media, including the risks associated with its use, the value that it creates, and the negative stigma attached to it within workplaces. The use of social media for information sharing during critical events as well as for seeking and/or rendering help has also been investigated in prior research. Other contexts include political and public administration, and the comparison between traditional and social media. Overall, our study identifies multiple emergent themes in the existing corpus, thereby furthering our understanding of advances in social media research. The integrated view of the extant literature that our study presents can help avoid duplication by future researchers, whilst offering fruitful lines of enquiry to help shape research for this emerging field.