Matching Items (54)
Description
Analysis of social networks has the potential to provide insights into wide range of applications. As datasets continue to grow, a key challenge is the lack of a widely applicable algorithmic framework for detection of statistically anomalous networks and network properties. Unlike traditional signal processing, where models of truth or empirical verification and background data exist and are often well defined, these features are commonly lacking in social and other networks. Here, a novel algorithmic framework for statistical signal processing for graphs is presented. The framework is based on the analysis of spectral properties of the residuals matrix. The framework is applied to the detection of innovation patterns in publication networks, leveraging well-studied empirical knowledge from the history of science. Both the framework itself and the application constitute novel contributions, while advancing algorithmic and mathematical techniques for graph-based data and understanding of the patterns of emergence of novel scientific research. Results indicate the efficacy of the approach and highlight a number of fruitful future directions.
ContributorsBliss, Nadya Travinin (Author) / Laubichler, Manfred (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2015
Description
This dissertation explored how immigrants cope with and thrive in old age by utilizing social networks, and the hindrances which may prevent this. Through ethnographic fieldwork and in-depth interviews at two senior centers in Phoenix, Arizona with a high concentration of an ethnic minority group - Asian and Latino, I describe what makes the Asian dominant center more resource abundant than its Latino counterpart given prevalent tight public funding. Both centers have a large number of seniors disenfranchised from mainstream institutions who bond together via similar experiences resulting from shared countries/regions of origin, language, and migration experience. The Asian center, however, is more successful in generating and circulating resources through "bonding" and "bridging" older immigrants who, therefore benefit more from their center affiliation than the Latinos at their center.
The abundance of resources at the Asian center flowing to the social networks of seniors are attributed to three factors: work and volunteer engagement and history, the organization of the center, and individual activities. At both centers seniors bond with each other due to shared ethnicity, language, and migration experience and share information and companionship in the language in which they feel most comfortable. What differentiated the two centers were the presence of several people well connected to individuals, groups, and institutions beyond the affiliated center. The presence of these "bridges" were critical when the centers were faced with budgetary constraints and Arizona was experiencing the effect of ongoing immigration policies. These "bridges" tend to come from shared ethnicity, and better social positions due to cumulative factors which include but are not limited to higher education, professional occupation, and work and volunteer history. I have also presented cases of individuals who, although have developed expertise from past work experiences and individual activities, have limited contribution to the resource flow because of the differences in ethnicity. The study also explored a gendered life course and its impact on the social network for older Asian and Latino immigrants.
The abundance of resources at the Asian center flowing to the social networks of seniors are attributed to three factors: work and volunteer engagement and history, the organization of the center, and individual activities. At both centers seniors bond with each other due to shared ethnicity, language, and migration experience and share information and companionship in the language in which they feel most comfortable. What differentiated the two centers were the presence of several people well connected to individuals, groups, and institutions beyond the affiliated center. The presence of these "bridges" were critical when the centers were faced with budgetary constraints and Arizona was experiencing the effect of ongoing immigration policies. These "bridges" tend to come from shared ethnicity, and better social positions due to cumulative factors which include but are not limited to higher education, professional occupation, and work and volunteer history. I have also presented cases of individuals who, although have developed expertise from past work experiences and individual activities, have limited contribution to the resource flow because of the differences in ethnicity. The study also explored a gendered life course and its impact on the social network for older Asian and Latino immigrants.
ContributorsFukui, Haruna Miyagawa (Author) / Menjivar, Cecilia (Thesis advisor) / Glick, Jennifer E. (Committee member) / McHugh, Kevin (Committee member) / Arizona State University (Publisher)
Created2014
Description
The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting. When exploring the city and neighborhood, the increasing number of POIs could enrich people's daily life, providing them with more choices of life experience than before, while at the same time also brings the problem of "curse of choices", resulting in the difficulty for a user to make a satisfied decision on "where to go" in an efficient way. Personalized POI recommendation is a task proposed on purpose of helping users filter out uninteresting POIs and reduce time in decision making, which could also benefit virtual marketing.
Developing POI recommender systems requires observation of human mobility w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to "check in" at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobility for personalized POI recommendation in spatial, temporal, social, and content aspects.
Different from recommender systems in other categories, e.g., movie recommendation in NetFlix, friend recommendation in dating websites, item recommendation in online shopping sites, personalized POI recommendation on LBSNs has its unique challenges due to the stochastic property of human mobility and the mobile behavior indications provided by LBSN information layout. The strong correlations between geographical POI information and other LBSN information result in three major human mobile properties, i.e., geo-social correlations, geo-temporal patterns, and geo-content indications, which are neither observed in other recommender systems, nor exploited in current POI recommendation. In this dissertation, we investigate these properties on LBSNs, and propose personalized POI recommendation models accordingly. The performance evaluated on real-world LBSN datasets validates the power of these properties in capturing user mobility, and demonstrates the ability of our models for personalized POI recommendation.
Developing POI recommender systems requires observation of human mobility w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to "check in" at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobility for personalized POI recommendation in spatial, temporal, social, and content aspects.
Different from recommender systems in other categories, e.g., movie recommendation in NetFlix, friend recommendation in dating websites, item recommendation in online shopping sites, personalized POI recommendation on LBSNs has its unique challenges due to the stochastic property of human mobility and the mobile behavior indications provided by LBSN information layout. The strong correlations between geographical POI information and other LBSN information result in three major human mobile properties, i.e., geo-social correlations, geo-temporal patterns, and geo-content indications, which are neither observed in other recommender systems, nor exploited in current POI recommendation. In this dissertation, we investigate these properties on LBSNs, and propose personalized POI recommendation models accordingly. The performance evaluated on real-world LBSN datasets validates the power of these properties in capturing user mobility, and demonstrates the ability of our models for personalized POI recommendation.
ContributorsGao, Huiji (Author) / Liu, Huan (Thesis advisor) / Xue, Guoliang (Committee member) / Ye, Jieping (Committee member) / Caverlee, James (Committee member) / Arizona State University (Publisher)
Created2014
Description
Social media platforms such as Twitter, Facebook, and blogs have emerged as valuable
- in fact, the de facto - virtual town halls for people to discover, report, share and
communicate with others about various types of events. These events range from
widely-known events such as the U.S Presidential debate to smaller scale, local events
such as a local Halloween block party. During these events, we often witness a large
amount of commentary contributed by crowds on social media. This burst of social
media responses surges with the "second-screen" behavior and greatly enriches the
user experience when interacting with the event and people's awareness of an event.
Monitoring and analyzing this rich and continuous flow of user-generated content can
yield unprecedentedly valuable information about the event, since these responses
usually offer far more rich and powerful views about the event that mainstream news
simply could not achieve. Despite these benefits, social media also tends to be noisy,
chaotic, and overwhelming, posing challenges to users in seeking and distilling high
quality content from that noise.
In this dissertation, I explore ways to leverage social media as a source of information and analyze events based on their social media responses collectively. I develop, implement and evaluate EventRadar, an event analysis toolbox which is able to identify, enrich, and characterize events using the massive amounts of social media responses. EventRadar contains three automated, scalable tools to handle three core event analysis tasks: Event Characterization, Event Recognition, and Event Enrichment. More specifically, I develop ET-LDA, a Bayesian model and SocSent, a matrix factorization framework for handling the Event Characterization task, i.e., modeling characterizing an event in terms of its topics and its audience's response behavior (via ET-LDA), and the sentiments regarding its topics (via SocSent). I also develop DeMa, an unsupervised event detection algorithm for handling the Event Recognition task, i.e., detecting trending events from a stream of noisy social media posts. Last, I develop CrowdX, a spatial crowdsourcing system for handling the Event Enrichment task, i.e., gathering additional first hand information (e.g., photos) from the field to enrich the given event's context.
Enabled by EventRadar, it is more feasible to uncover patterns that have not been
explored previously and re-validating existing social theories with new evidence. As a
result, I am able to gain deep insights into how people respond to the event that they
are engaged in. The results reveal several key insights into people's various responding
behavior over the event's timeline such the topical context of people's tweets does not
always correlate with the timeline of the event. In addition, I also explore the factors
that affect a person's engagement with real-world events on Twitter and find that
people engage in an event because they are interested in the topics pertaining to
that event; and while engaging, their engagement is largely affected by their friends'
behavior.
- in fact, the de facto - virtual town halls for people to discover, report, share and
communicate with others about various types of events. These events range from
widely-known events such as the U.S Presidential debate to smaller scale, local events
such as a local Halloween block party. During these events, we often witness a large
amount of commentary contributed by crowds on social media. This burst of social
media responses surges with the "second-screen" behavior and greatly enriches the
user experience when interacting with the event and people's awareness of an event.
Monitoring and analyzing this rich and continuous flow of user-generated content can
yield unprecedentedly valuable information about the event, since these responses
usually offer far more rich and powerful views about the event that mainstream news
simply could not achieve. Despite these benefits, social media also tends to be noisy,
chaotic, and overwhelming, posing challenges to users in seeking and distilling high
quality content from that noise.
In this dissertation, I explore ways to leverage social media as a source of information and analyze events based on their social media responses collectively. I develop, implement and evaluate EventRadar, an event analysis toolbox which is able to identify, enrich, and characterize events using the massive amounts of social media responses. EventRadar contains three automated, scalable tools to handle three core event analysis tasks: Event Characterization, Event Recognition, and Event Enrichment. More specifically, I develop ET-LDA, a Bayesian model and SocSent, a matrix factorization framework for handling the Event Characterization task, i.e., modeling characterizing an event in terms of its topics and its audience's response behavior (via ET-LDA), and the sentiments regarding its topics (via SocSent). I also develop DeMa, an unsupervised event detection algorithm for handling the Event Recognition task, i.e., detecting trending events from a stream of noisy social media posts. Last, I develop CrowdX, a spatial crowdsourcing system for handling the Event Enrichment task, i.e., gathering additional first hand information (e.g., photos) from the field to enrich the given event's context.
Enabled by EventRadar, it is more feasible to uncover patterns that have not been
explored previously and re-validating existing social theories with new evidence. As a
result, I am able to gain deep insights into how people respond to the event that they
are engaged in. The results reveal several key insights into people's various responding
behavior over the event's timeline such the topical context of people's tweets does not
always correlate with the timeline of the event. In addition, I also explore the factors
that affect a person's engagement with real-world events on Twitter and find that
people engage in an event because they are interested in the topics pertaining to
that event; and while engaging, their engagement is largely affected by their friends'
behavior.
ContributorsHu, Yuheng (Author) / Kambhampati, Subbarao (Thesis advisor) / Horvitz, Eric (Committee member) / Krumm, John (Committee member) / Liu, Huan (Committee member) / Sundaram, Hari (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis proposed a novel approach to establish the trust model in a social network scenario based on users' emails. Email is one of the most important social connections nowadays. By analyzing email exchange activities among users, a social network trust model can be established to judge the trust rate between each two users. The whole trust checking process is divided into two steps: local checking and remote checking. Local checking directly contacts the email server to calculate the trust rate based on user's own email communication history. Remote checking is a distributed computing process to get help from user's social network friends and built the trust rate together. The email-based trust model is built upon a cloud computing framework called MobiCloud. Inside MobiCloud, each user occupies a virtual machine which can directly communicate with others. Based on this feature, the distributed trust model is implemented as a combination of local analysis and remote analysis in the cloud. Experiment results show that the trust evaluation model can give accurate trust rate even in a small scale social network which does not have lots of social connections. With this trust model, the security in both social network services and email communication could be improved.
ContributorsZhong, Yunji (Author) / Huang, Dijiang (Thesis advisor) / Dasgupta, Partha (Committee member) / Syrotiuk, Violet (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation explores the interrelationships between periods of rapid social change and regional-scale social identities. Using archaeological data from the Cibola region of the U.S. Southwest, I examine changes in the nature and scale of social identification across a period of demographic and social upheaval (A.D. 1150-1325) marked by a shift from dispersed hamlets, to clustered villages, and eventually, to a small number of large nucleated towns. This transformation in settlement organization entailed a fundamental reconfiguration of the relationships among households and communities across an area of over 45,000 km2. This study draws on contemporary social theory focused on political mobilization and social movements to investigate how changes in the process of social identification can influence the potential for such widespread and rapid transformations. This framework suggests that social identification can be divided into two primary modes; relational identification based on networks of interaction among individuals, and categorical identification based on active expressions of affiliation with social roles or groups to which one can belong. Importantly, trajectories of social transformations are closely tied to the interrelationships between these two modes of identification. This study has three components: Social transformation, indicated by rapid demographic and settlement transitions, is documented through settlement studies drawing on a massive, regional database including over 1,500 sites. Relational identities, indicated by networks of interaction, are documented through ceramic compositional analyses of over 2,100 potsherds, technological characterizations of over 2,000 utilitarian ceramic vessels, and the distributions of different types of domestic architectural features across the region. Categorical identities are documented through stylistic comparisons of a large sample of polychrome ceramic vessels and characterizations of public architectural spaces. Contrary to assumptions underlying traditional approaches to social identity in archaeology, this study demonstrates that relational and categorical identities are not necessarily coterminous. Importantly, however, the strongest patterns of relational connections prior to the period of social transformation in the Cibola region largely predict the scale and structure of changes associated with that transformation. This suggests that the social transformation in the Cibola region, despite occurring in a non-state setting, was governed by similar dynamics to well-documented contemporary examples.
ContributorsPeeples, Matthew A. (Author) / Kintigh, Keith W. (Thesis advisor) / Hegmon, Michelle (Thesis advisor) / Spielmann, Katherine A. (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis deals with the analysis of interpersonal communication dynamics in online social networks and social media. Our central hypothesis is that communication dynamics between individuals manifest themselves via three key aspects: the information that is the content of communication, the social engagement i.e. the sociological framework emergent of the communication process, and the channel i.e. the media via which communication takes place. Communication dynamics have been of interest to researchers from multi-faceted domains over the past several decades. However, today we are faced with several modern capabilities encompassing a host of social media websites. These sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information, our modes of social engagement, and mechanisms of how the media characteristics impact our interactional behavior. The outcomes of this research are manifold. We present our contributions in three parts, corresponding to the three key organizing ideas. First, we have observed that user context is key to characterizing communication between a pair of individuals. However interestingly, the probability of future communication seems to be more sensitive to the context compared to the delay, which appears to be rather habitual. Further, we observe that diffusion of social actions in a network can be indicative of future information cascades; that might be attributed to social influence or homophily depending on the nature of the social action. Second, we have observed that different modes of social engagement lead to evolution of groups that have considerable predictive capability in characterizing external-world temporal occurrences, such as stock market dynamics as well as collective political sentiments. Finally, characterization of communication on rich media sites have shown that conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Based on all these outcomes, we believe that this research can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.
ContributorsDe Choudhury, Munmun (Author) / Sundaram, Hari (Thesis advisor) / Candan, K. Selcuk (Committee member) / Liu, Huan (Committee member) / Watts, Duncan J. (Committee member) / Seligmann, Doree D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Access control is one of the most fundamental security mechanisms used in the design and management of modern information systems. However, there still exists an open question on how formal access control models can be automatically analyzed and fully realized in secure system development. Furthermore, specifying and managing access control policies are often error-prone due to the lack of effective analysis mechanisms and tools. In this dissertation, I present an Assurance Management Framework (AMF) that is designed to cope with various assurance management requirements from both access control system development and policy-based computing. On one hand, the AMF framework facilitates comprehensive analysis and thorough realization of formal access control models in secure system development. I demonstrate how this method can be applied to build role-based access control systems by adopting the NIST/ANSI RBAC standard as an underlying security model. On the other hand, the AMF framework ensures the correctness of access control policies in policy-based computing through automated reasoning techniques and anomaly management mechanisms. A systematic method is presented to formulate XACML in Answer Set Programming (ASP) that allows users to leverage off-the-shelf ASP solvers for a variety of analysis services. In addition, I introduce a novel anomaly management mechanism, along with a grid-based visualization approach, which enables systematic and effective detection and resolution of policy anomalies. I further evaluate the AMF framework through modeling and analyzing multiparty access control in Online Social Networks (OSNs). A MultiParty Access Control (MPAC) model is formulated to capture the essence of multiparty authorization requirements in OSNs. In particular, I show how AMF can be applied to OSNs for identifying and resolving privacy conflicts, and representing and reasoning about MPAC model and policy. To demonstrate the feasibility of the proposed methodology, a suite of proof-of-concept prototype systems is implemented as well.
ContributorsHu, Hongxin (Author) / Ahn, Gail-Joon (Thesis advisor) / Yau, Stephen S. (Committee member) / Dasgupta, Partha (Committee member) / Ye, Nong (Committee member) / Arizona State University (Publisher)
Created2012
Description
With more than 70 percent of the world's population expected to live in cities by 2050, it behooves us to understand urban sustainability and improve the capacity of city planners and policymakers to achieve sustainable goals. Producing and linking knowledge to action is a key tenet of sustainability science. This dissertation examines how knowledge-action systems -- the networks of actors involved in the production, sharing and use of policy-relevant knowledge -- work in order to inform what capacities are necessary to effectively attain sustainable outcomes. Little is known about how knowledge-action systems work in cities and how they should be designed to address their complexity. I examined this question in the context of land use and green area governance in San Juan, Puerto Rico, where political conflict exists over extensive development, particularly over the city's remaining green areas. I developed and applied an interdisciplinary framework -- the Knowledge-Action System Analysis (KASA) Framework --that integrates concepts of social network analysis and knowledge co-production (i.e., epistemic cultures and boundary work). Implementation of the framework involved multiple methods --surveys, interviews, participant observations, and document--to gather and analyze quantitative and qualitative data. Results from the analysis revealed a diverse network of actors contributing different types of knowledge, thus showing a potential in governance for creativity and innovation. These capacities, however, are hindered by various political and cultural factors, such as: 1) breakdown in vertical knowledge flow between state, city, and local actors; 2) four divergent visions of San Juan's future emerging from distinct epistemic cultures; 3) extensive boundary work by multiple actors to separate knowledge and planning activities, and attain legitimacy and credibility in the process; 4) and hierarchies of knowledge where outside expertise (e.g., private planning and architectural firms) is privileged over others, thus reflecting competing knowledge systems in land use and green area planning in San Juan. I propose a set of criteria for building just and effective knowledge-action systems for cities, including: context and inclusiveness, adaptability and reflexivity, and polycentricity. In this way, this study also makes theoretical contributions to the knowledge systems literature specifically, and urban sustainability in general.
ContributorsMuñoz-Erickson, Tischa A (Author) / Larson, Kelli L. (Thesis advisor) / Redman, Charles L. (Thesis advisor) / Miller, Clark A. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Social structure affects many aspects of ecology including mating systems, dispersal, and movements. The quality and pattern of associations among individuals can define social structure, thus detailed behavioral observations are vital to understanding species social structure and many other aspects of their ecology. In squamate reptiles (lizards and snakes), detailed observations of associations among individuals have been primarily limited to several lineages of lizards and have revealed a variety of social structures, including polygynous family group-living and monogamous pair-living. Here I describe the social structure of two communities within a population of Arizona black rattlesnakes (Crotalus cerberus) using association indices and social network analysis. I used remote timelapse cameras to semi-continuously sample rattlesnake behavior at communal basking sites during early April through mid-May in 2011 and 2012. I calculated an association index for each dyad (proportion of time they spent together) and used these indices to construct a weighted, undirected social network for each community. I found that individual C. cerberus vary in their tendency to form associations and are selective about with whom they associate. Some individuals preferred to be alone or in small groups while others preferred to be in large groups. Overall, rattlesnakes exhibited non-random association patterns, and this result was mainly driven by association selection of adults. Adults had greater association strengths and were more likely to have limited and selected associates. I identified eight subgroups within the two communities (five in one, three in the other), all of which contained adults and juveniles. My study is the first to show selected associations among individual snakes, but to my knowledge it is also the first to use association indices and social network analysis to examine association patterns among snakes. When these methods are applied to other snake species that aggregate, I anticipate the `discovery' of similar social structures.
ContributorsAmarello, Melissa (Author) / DeNardo, Dale F (Thesis advisor) / Sullivan, Brian K. (Committee member) / Schuett, Gordon W. (Committee member) / Arizona State University (Publisher)
Created2012