Matching Items (25)
Description
In most social networking websites, users are allowed to perform interactive activities. One of the fundamental features that these sites provide is to connecting with users of their kind. On one hand, this activity makes online connections visible and tangible; on the other hand, it enables the exploration of our connections and the expansion of our social networks easier. The aggregation of people who share common interests forms social groups, which are fundamental parts of our social lives. Social behavioral analysis at a group level is an active research area and attracts many interests from the industry. Challenges of my work mainly arise from the scale and complexity of user generated behavioral data. The multiple types of interactions, highly dynamic nature of social networking and the volatile user behavior suggest that these data are complex and big in general. Effective and efficient approaches are required to analyze and interpret such data. My work provide effective channels to help connect the like-minded and, furthermore, understand user behavior at a group level. The contributions of this dissertation are in threefold: (1) proposing novel representation of collective tagging knowledge via tag networks; (2) proposing the new information spreader identification problem in egocentric soical networks; (3) defining group profiling as a systematic approach to understanding social groups. In sum, the research proposes novel concepts and approaches for connecting the like-minded, enables the understanding of user groups, and exposes interesting research opportunities.
ContributorsWang, Xufei (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
The current study examines the social structure of local street gangs in Glendale, Arizona. Literature on gang organization has come to different conclusions about gang organization, largely based on the methodology used. One consistent finding from qualitative gang research has been that understanding the social connections between gang members is important for understanding how gangs are organized. The current study examines gang social structure by recreating gang social networks using official police data. Data on documented gang members, arrest records, and field interview cards from a 5-year period from 2006 to 2010 were used. Yearly social networks were constructed going two steps out from documented gang members. The findings indicated that gang networks had high turnover and they consisted of small subgroups. Further, the position of the gang member or associate was a significant predictor of arrest, specifically for those who had high betweenness centrality. At the group level, density and measures of centralization were not predictive of group-level behavior; hybrid groups were more likely to be involved in criminal behavior, however. The implications of these findings for both theory and policy are discussed.
ContributorsFox, Andrew (Author) / Katz, Charles M. (Thesis advisor) / White, Michael D. (Committee member) / Sweeten, Gary (Committee member) / Arizona State University (Publisher)
Created2013
Description
Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question. In this thesis, I develop a framework to personalize social signals for users to guide their activities on an online platform. As the result, we gradually nudge the activity distribution on the platform from the initial distribution p to the target distribution q. My work is particularly applicable to guiding collaborations, guiding collective actions, and online advertising. In particular, I first propose a probabilistic model on how users behave and how information flows on the platform. The main part of this thesis after that discusses the Influence Individuals through Social Signals (IISS) framework. IISS consists of four main components: (1) Learner: it learns users' interests and characteristics from their historical activities using Bayesian model, (2) Calculator: it uses gradient descent method to compute the intermediate activity distributions, (3) Selector: it selects users who can be influenced to adopt or drop specific activities, (4) Designer: it personalizes social signals for each user. I evaluate the performance of IISS framework by simulation on several network topologies such as preferential attachment, small world, and random. I show that the framework gradually nudges users' activities to approach the target distribution. I use both simulation and mathematical method to analyse convergence properties such as how fast and how close we can approach the target distribution. When the number of activities is 3, I show that for about 45% of target distributions, we can achieve KL-divergence as low as 0.05. But for some other distributions KL-divergence can be as large as 0.5.
ContributorsLe, Tien D (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014
Description
With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to easily and effectively reach out to people, interact with them, influence them, or get their opinions. There are two pieces of information that attract most attention on social media sites, including user preferences and interactions. Businesses and organizations use this information to better understand and therefore provide customized services to social media users. This data can be used for different purposes such as, targeted advertisement, product recommendation, or even opinion mining. Social media sites use this information to better serve their users.
Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.
Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.
ContributorsAbbasi, Mohammad Ali, 1975- (Author) / Liu, Huan (Thesis advisor) / Davulcu, Hasan (Committee member) / Ye, Jieping (Committee member) / Agarwal, Nitin (Committee member) / Arizona State University (Publisher)
Created2014
Description
This study examined the enactment of a high school district's college-going mission. Treating mission enactment as a case of policy implementation, this study used the lens of complexity theory to understand how system actors and contexts influenced variation and adaptation. Data collection methods included observations, interviews, focus groups, and surveys of various system actors including district staff, principals, counselors, teachers, and students. This study used a mixed methods analytic inductive technique and Social Network Analysis to describe the mission's implementation. Findings reflect that the mission was a vaguely defined value statement; school staff reacted to the mission with limited buy-in and confusion about what it really meant in practice. The mission lacked clear boundaries of what constituted related programs or policies. Consequently, in this site-based district, schools unevenly implemented related programs and policies. School staff wanted more guidance from district staff and clear expectations for mission-related actions. To help meet this need, the district was moving to a more centralized, hierarchical approach. Though they were providing information about the mission, district staff were not providing specific, responsive support to organize school staff's efforts around implementation. District staff were trying to find an approach that both supported schools towards a common vision and provided flexibility for school-level adaptations. Yet, the district had not yet fully formed its position as a facilitator of implementation. Further, as the district lacked a cohesive measurement system, the effectiveness of this initiative was unknown. This study sought to present policy implementation as varied phenomenon, influenced by system actors and conditions. Findings suggest that while policy cannot determine actions, district staff could help create conditions that would support implementation.
ContributorsDunn, Lenay Danielle (Author) / Berliner, David (Thesis advisor) / Danzig, Arnold (Committee member) / Smith, Mary Lee (Committee member) / Arizona State University (Publisher)
Created2011
Description
Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model is developed to predict the emergence of viral hashtag breakouts, using network features. Next, another model is developed to detect and compare individual and organizational accounts, by using a set of domain and language-independent features. The third model exposes contentious issues, driving reactionary dynamics between opposing camps. The fourth model develops community detection and visualization methods to reveal underlying dynamics and key messages that drive dynamics. The final model presents a use case methodology for detecting and monitoring foreign influence, wherein a state actor and news media under its control attempt to shift public opinion by framing information to support multiple adversarial narratives that facilitate their goals. In each case, a discussion of novel aspects and contributions of the models is presented, as well as quantitative and qualitative evaluations. An analysis of multiple conflict situations will be conducted, covering areas in the UK, Bangladesh, Libya and the Ukraine where adversarial framing lead to polarization, declines in social cohesion, social unrest, and even civil wars (e.g., Libya and the Ukraine).
ContributorsAlzahrani, Sultan (Author) / Davulcu, Hasan (Thesis advisor) / Corman, Steve R. (Committee member) / Li, Baoxin (Committee member) / Hsiao, Ihan (Committee member) / Arizona State University (Publisher)
Created2018
Description
In the artificial intelligence literature, three forms of reasoning are commonly employed to understand agent behavior: inductive, deductive, and abductive. More recently, data-driven approaches leveraging ideas such as machine learning, data mining, and social network analysis have gained popularity. While data-driven variants of the aforementioned forms of reasoning have been applied separately, there is little work on how data-driven approaches across all three forms relate and lend themselves to practical applications. Given an agent behavior and the percept sequence, how one can identify a specific outcome such as the likeliest explanation? To address real-world problems, it is vital to understand the different types of reasonings which can lead to better data-driven inference.
This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks.
This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks.
ContributorsShaabani, Elham (Author) / Shakarian, Paulo (Thesis advisor) / Davulcu, Hasan (Committee member) / Maciejewski, Ross (Committee member) / Decker, Scott (Committee member) / Arizona State University (Publisher)
Created2019
Description
Social structure is the product of the costs and benefits of group living. Dyadic social bonds in female chacma baboons are strong and long-standing, conferring fitness benefits upon both individuals while contributing to a greater social structure. Longitudinal grooming data collected from 2001-2007 from Moremi Game Reserve, Botswana, illuminate social network dynamics of 50 female chacma baboons. Utilizing social network analysis (SNA), we analyzed social structure above the level of the dyad to see if attribute data (age, rank, and number of close female kin) was predictive of network location. Our SNA data was longitudinal, unbalanced, and continuous. We therefore used linear mixed-effects models (LMEs) and respective AIC/BIC values to choose the most likely predictive attributes for each SNA metric. From the chosen LMEs, rank was present most often. High rank predicted a higher frequency of outward grooming, an overall lower number of grooming partners, and a less extensive social network. It appears that high-ranking females have a fewer number of social bonds than low-ranking females, but that they are stronger. Considering that enduring social bonds result in increased offspring longevity, future studies include examining the potential adaptive value of weak, transient, more numerous social bonds.
ContributorsBest, Megan Renee (Author) / Silk, Joan B. (Thesis director) / Schaefer, David (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution and Social Change (Contributor) / School of Life Sciences (Contributor)
Created2014-05
Description
People are motivated to participate in musical activities for many reasons. Whereas musicians may be driven by an intrinsic desire for musical growth, self-determination theory suggests that this drive must also be sustained and supported by the social environment. Social network analysis is an interdisciplinary theoretical framework and collection of analytical methods that allows us to describe the social context of a musical ensemble. These frameworks are utilized to investigate the relationship of participatory motivation and social networks in a large Division I collegiate marching band. This study concludes that marching band members are predominantly self-determined to participate in marching band and are particularly motivated for social reasons, regardless of their experience over the course of the band season. The members who are highly motived are also more integrated into the band's friendship and advice networks. These highly integrated members also tend to be motivated by the value and importance others display for the marching band activity suggesting these members have begun to internalized those values and seek out others with similar viewpoints. These findings highlight the central nature of the social experience of marching band and have possible implications for other musical leisure ensembles. After a brief review of social music making and the theoretical frameworks, I will provide illustrations of the relationship between motivation and social networks in a musical ensemble, consider the implications of these findings for promoting self-determined motivation and the wellbeing of musical ensembles, and identify directions for future research.
ContributorsWeren, Serena (Author) / Hill, Gary W. (Thesis advisor) / Granger, Douglas (Committee member) / Bailey, Wayne (Committee member) / Norton, Kay (Committee member) / Reber, William (Committee member) / Arizona State University (Publisher)
Created2015
Description
Unmanned aerial vehicles have received increased attention in the last decade due to their versatility, as well as the availability of inexpensive sensors (e.g. GPS, IMU) for their navigation and control. Multirotor vehicles, specifically quadrotors, have formed a fast growing field in robotics, with the range of applications spanning from surveil- lance and reconnaissance to agriculture and large area mapping. Although in most applications single quadrotors are used, there is an increasing interest in architectures controlling multiple quadrotors executing a collaborative task. This thesis introduces a new concept of control involving more than one quadrotors, according to which two quadrotors can be physically coupled in mid-flight. This concept equips the quadro- tors with new capabilities, e.g. increased payload or pursuit and capturing of other quadrotors. A comprehensive simulation of the approach is built to simulate coupled quadrotors. The dynamics and modeling of the coupled system is presented together with a discussion regarding the coupling mechanism, impact modeling and additional considerations that have been investigated. Simulation results are presented for cases of static coupling as well as enemy quadrotor pursuit and capture, together with an analysis of control methodology and gain tuning. Practical implementations are introduced as results show the feasibility of this design.
ContributorsLarsson, Daniel (Author) / Artemiadis, Panagiotis (Thesis advisor) / Marvi, Hamidreza (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2016