abstract: 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. / Dissertation/Thesis / Doctoral Dissertation Sociology 2014

abstract: 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. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2014

abstract: 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. / Dissertation/Thesis / Doctoral Dissertation Music 2015

abstract: Background: College freshmen are exposed to a variety of environmental and social factors that can alter changes to health habits and encourage weight gain. Weight-related conversations had with friends may be related to self-perception of weight and alterations to health behaviors, but this association has yet to be assessed in the college population. Objective: This study aims to examine the relationship between friend advice about weight management, self-perception of weight, and alterations to weight change intentions, physical activity, and eating habits in college freshmen over time. Methods: College freshmen from ASU with complete data for three time points (n=321) were found to be predominantly female (72.2%) and non-white (53.2%) with a mean age of 17.5±41. Complete data included responses for items included in analysis which were related to friend encouragement about weigh management, self-perception of weight, physical activity, eating behaviors, and weight change intentions. A longitudinal multivariate mediation analysis using negative binomial regression adjusted for sociodemographics and clustering by dorm was used to assess the relationship between 1) friend encouragement about weight management at time 1 and behavioral outcomes at time 3, 2) friend encouragement about weight management at time 1 and self-perception of weight at time 2, and 3) self-perception of weight at time 2 and behavioral outcomes at time 3. Results: A small proportion of population perceived friend encouragement about weight loss (18.3%) and weight gain (14.4%) at time 1. Half the population (50.9%) had the self-perception of overweight at time 2. At time 3, more than half (54.3%) of individuals performed at least 60 minutes of MVPA and consumed at least ½ a serving of sugar-sweetened beverages each day, while nearly half (48.6%) consumed at least 2 servings of fruits and vegetables each day. Males perceived more friend encouragement to gain weight (27.4%; p<0.01), but more females had the self-perception of overweight (54%; p=0.04) and were attempting to lose weight (59.3%; p<0.01). Individuals who perceived friend encouragement to lose weight at time 1 had a 14.8% greater prevalence (p<0.001) of overweight perception of time two, and a 9.6% and 6.9%; decreased prevalence (p<0.001) of weight change and weight loss intentions (p=0.023) at time three respectively. Individuals who perceived friend encouragement to gain weight had a 34.9% decreased prevalence of (p<0.001) of self-perception of overweight at time 1. In individuals with the self-perception of overweight at time 2, there was a 18.1% increased prevalence (p<0.001) of consuming at least ½ a serving of sugar-sweetened beverages/day and an increased prevalence of 22.8% and 24.0% for weight change intentions and weight loss intentions at time 3 (p<0.001). Conclusion: These findings suggest that there was not a mediation effect of self-perception of overweight in the relationship between friend encouragement about weight management and behavioral outcomes in the current sample. However, the increased prevalence of overweight perception in individuals who perceived friend encouragement about weight management may inform future interventions to focus on how weight-related conversations with friends is related to overweight perception. More research about the relationship between weight-related conversations had with friends, self-perception of weight, and health behaviors is needed to confirm these findings. / Dissertation/Thesis / Masters Thesis Nutrition 2016

abstract: Many models of colonial interaction are build from cases of European colonialism among Native American and African peoples, and, as a result, they are often ill-suited to account for state expansion and decline in non-Western contexts. This dissertation investigates social organization and intraregional interaction in a non-western colonial context to broaden understanding of colonial interaction in diverse sociocultural settings. Drawing on social identity theory, population genetics, and social network analysis, patterns of social organization at the margins of the expansive pre-Hispanic Tiwanaku state (ca. AD 500-1100) are examined. According to the dual diaspora model of Tiwanaku colonial organization in the Moquegua Valley of southern Peru, Chen Chen-style and Omo-style ethnic communities who colonized the valley maintained distinct ethnic identities in part through endogamous marriage practices. Biodistance analysis of cranial shape data is used to evaluate regional gene flow among Tiwanaku-affiliated communities in Moquegua. Overall, results of biodistance analysis are consistent with the dual diaspora model. Omo- and Chen Chen-style communities are distinct in mean cranial shape, and it appears that ethnic identity structured gene flow between ethnic groups. However, there are notable exceptions to the overall pattern, and it appears that marriage practices were structured by multiple factors, including ethnic affiliation, geographic proximity, and smaller scales of social organization, such as corporate kin groups. Social network analysis of cranial shape data is used to implement a multi- and mesoscalar approach to social organization to assess family-based organization at a regional level. Results indicate the study sample constituted a social network comprised of a dense main component and a number of isolated actors. Formal approaches for identifying potential family groups (i.e., subgroup analysis) proved more effective than informal approaches. While there is no clear partition of the network into distinct subgroups that could represent extended kin networks or biological lineages, there is a cluster of closely related individuals at the core of the network who integrate a web of less-closely related actors. Subgroup analysis yielded similar results as agglomerative hierarchical cluster analysis, which suggests there is potential for social network analysis to contribute to bioarchaeological studies of social organization and bioarchaeological research in general. / Dissertation/Thesis / Doctoral Dissertation Anthropology 2016

abstract: Due to vast resources brought by social media services, social data mining has received increasing attention in recent years. The availability of sheer amounts of user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information in social networks could provide another rich source in deriving implicit information for social data mining. However, the vast majority of existing studies overwhelmingly focus on positive links between users while negative links are also prevailing in real- world social networks such as distrust relations in Epinions and foe links in Slashdot. Though recent studies show that negative links have some added value over positive links, it is dicult to directly employ them because of its distinct characteristics from positive interactions. Another challenge is that label information is rather limited in social media as the labeling process requires human attention and may be very expensive. Hence, alternative criteria are needed to guide the learning process for many tasks such as feature selection and sentiment analysis. To address above-mentioned issues, I study two novel problems for signed social networks mining, (1) unsupervised feature selection in signed social networks; and (2) unsupervised sentiment analysis with signed social networks. To tackle the first problem, I propose a novel unsupervised feature selection framework SignedFS. In particular, I model positive and negative links simultaneously for user preference learning, and then embed the user preference learning into feature selection. To study the second problem, I incorporate explicit sentiment signals in textual terms and implicit sentiment signals from signed social networks into a coherent model Signed- Senti. Empirical experiments on real-world datasets corroborate the effectiveness of these two frameworks on the tasks of feature selection and sentiment analysis. / Dissertation/Thesis / Masters Thesis Computer Science 2017

abstract: With the rise of Online Social Networks (OSN) in the last decade, social network analysis has become a crucial research topic. The OSN graphs have unique properties that distinguish them from other types of graphs. In this thesis, five month Tweet corpus collected from Bangladesh - between June 2016 and October 2016 is analyzed, in order to detect accounts that belong to groups. These groups consist of official and non-official twitter handles of political organizations and NGOs in Bangladesh. A set of network, temporal, spatial and behavioral features are proposed to discriminate between accounts belonging to individual twitter users, news, groups and organization leaders. Finally, the experimental results are presented and a subset of relevant features is identified that lead to a generalizable model. Detection of tiny number of groups from large network is achieved with 0.8 precision, 0.75 recall and 0.77 F1 score. The domain independent network and behavioral features and models developed here are suitable for solving twitter account classification problem in any context. / Dissertation/Thesis / Masters Thesis Computer Science 2017

abstract: Our ability to understand networks is important to many applications, from the analysis and modeling of biological networks to analyzing social networks. Unveiling network dynamics allows us to make predictions and decisions. Moreover, network dynamics models have inspired new ideas for computational methods involving multi-agent cooperation, offering effective solutions for optimization tasks. This dissertation presents new theoretical results on network inference and multi-agent optimization, split into two parts - The first part deals with modeling and identification of network dynamics. I study two types of network dynamics arising from social and gene networks. Based on the network dynamics, the proposed network identification method works like a `network RADAR', meaning that interaction strengths between agents are inferred by injecting `signal' into the network and observing the resultant reverberation. In social networks, this is accomplished by stubborn agents whose opinions do not change throughout a discussion. In gene networks, genes are suppressed to create desired perturbations. The steady-states under these perturbations are characterized. In contrast to the common assumption of full rank input, I take a laxer assumption where low-rank input is used, to better model the empirical network data. Importantly, a network is proven to be identifiable from low rank data of rank that grows proportional to the network's sparsity. The proposed method is applied to synthetic and empirical data, and is shown to offer superior performance compared to prior work. The second part is concerned with algorithms on networks. I develop three consensus-based algorithms for multi-agent optimization. The first method is a decentralized Frank-Wolfe (DeFW) algorithm. The main advantage of DeFW lies on its projection-free nature, where we can replace the costly projection step in traditional algorithms by a low-cost linear optimization step. I prove the convergence rates of DeFW for convex and non-convex problems. I also develop two consensus-based alternating optimization algorithms --- one for least square problems and one for non-convex problems. These algorithms exploit the problem structure for faster convergence and their efficacy is demonstrated by numerical simulations. I conclude this dissertation by describing future research directions. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

This thesis addresses the aesthetics, ideologies and practicalities of contemporary European Improvised Music-making - this term referring to the tradition that emerged from 1960s American jazz and free jazz, and that remains, arguably, one of today's most misunderstood and under-represented musical genres. Using a multidisciplinary approach drawing on Grounded Theory, Ethnography and Social Network Analysis, and bounded by Berlin's cosmopolitan local scene of 2012-13, I define Improvised Music as a field of differing-yet-interconnected practices, and show how musicians and listeners conceived of and differentiated between these sub-styles, as well as how they discovered and learned to appreciate such a hidden, 'difficult' and idiosyncratic art form. Whilst on the surface Improvised Music might appear chaotic and beyond analysis in conventional terms, I show that, just like any other music, Improvised Music has its own genre-specific conventions, structures and expectations, and this research investigates its specific modes of performance, listening and appreciation - including the need to distinguish between 'musical' and 'processual' improvisatory outcomes, to differentiate between different 'levels' of improvising, and to separate the group and personal levels of the improvisatory process. I define improvised practices within this ifeld as variable combinations of 'composed' (pre-planned) and 'improvised' (real-time) elements, and examine the specific definitions of 'risk', 'honesty', 'trust', and 'good' and `bad' music-making which mediate these choices - these distinctions and evaluatory frameworks leading to a set of proposed conventions and distinctions for Improvised Music listening and production. This study looks at the representation of identity by improvising musicians, the use of social and political models as analogies for the improvisatory process (including the interplay between personal freedom of expression and the construction of coherent collective outcomes), and also examines the multiple functions of recording, in a music that was ostensibly only meant for the moment of its creation. All of this serves to address several popular misconceptions concerning Improvised Music, and does so directly from the point of view of a large sample of its most important practitioners and connoisseurs. Such findings provide key insights into the appreciation and understanding of Improvised Music itself (both for newcomers and those already adept in its ways), and this thesis offers important suggestions for scholars of Musicology, Ethnomusicology, Sociology of Music, Improvisation Studies, Performance Studies and Music/Cognitive Psychology, as well as for those concerned with improvisation and creativity in more general, non-musical, terms.

Les réseaux sociaux en ligne (OSNs) recueillent une masse de données à caractère privé. Le recueil de ces données ainsi que leur utilisation relèvent de nouveaux enjeux économiques et évoquent plusieurs questionnements notamment ceux relatifs à la protection de la vie privée. Notre thèse propose certaines réponses.Dans le premier chapitre nous analysons l'impact du partage des données personnelles de l'utilisateur sur sa vie privée. Tout d'abord, nous montrons comment les intérêts d'un utilisateur -- à titre d'exemple ses préférences musicales -- peuvent être à l'origine de fuite d'informations sensibles. Pour ce faire, nous inférons les attributs non divulgués du profil de l'utilisateur en exploitant d'autres profils partageant les même ''goûts musicaux''. Notre approche extrait la sémantique des intérêts en utilisant Wikipedia, les partitionne sémantiquement et enfin regroupe les utilisateurs ayant des intérêts semblables. Nos expérimentations réalisées sur plus de 104 milles profils publics collectés sur Facebook et plus de 2000 profils privés de bénévoles, montrent que notre technique d'inférence prédit efficacement les attributs qui sont très souvent cachés par les utilisateurs.Dans un deuxième temps, nous exposons les conséquences désastreuses du partage des données privées sur la sécurité. Nous nous focalisons sur les informations recueillies à partir de profils publics et comment celles-ci peuvent être exploitées pour accélérer le craquage des mots de passe. Premièrement, nous proposons un nouveau « craqueur » de mot de passe basé sur les chaînes de Markov permettant le cassage de plus de 80% des mots de passe, dépassant ainsi toutes les autres méthodes de l'état de l'art. Deuxièmement, et afin de mesurer l'impact sur la vie privée, nous proposons une méthodologie qui intègre les informations personnelles d'un utilisateur afin d'accélérer le cassage de ses mots de passe.Nos résultats mettent en évidence la nécessité de créer de nouvelles méthodes d'estimation des fuites d'informations personnelles, ce que nous proposons : il s'agit d'une méthode formelle pour estimer l'unicité de chaque profil en étudiant la quantité d'information portée par chaque attribut public.Notre travail se base sur la plate-forme publicitaire d'estimationd'utilisateurs de Facebook pour calculer l'entropie de chaque attribut public. Ce calcul permet d'évoluer l'impact du partage de ces informations publiquement. Nos résultats, basées sur un échantillon de plus de 400 mille profils publics Facebook, montrent que la combinaison de sexe, ville de résidence et age permet d'identifier d'une manière unique environ 18% des utilisateurs.Dans la deuxième section de notre thèse nous analysons les interactions entre la plate-forme du réseau social et des tiers et son impact sur à la vie privée des utilisateurs.Dans une première étude, nous explorons les capacités de « tracking » des réseaux sociaux Facebook, Google+ et Twitter. Nous étudions les mécanismes qui permettent à ces services de suivre d'une façon persistante l'activité web des utilisateurs ainsi que d'évaluer sa couverture. Nos résultats indiquent que le « tracking » utilisé par les OSNs couvre la quasi-totalité des catégories Web, indépendamment du contenu et de l'auditoire.Finalement, nous développons une plate-forme de mesure pour étudier l'interaction entre les plates-formes OSNs, les applications sociales et les « tierces parties » (e.g., fournisseurs de publicité). Nous démontrons que plusieurs applications tierces laissent filtrer des informations relatives aux utilisateurs à des tiers non autorisés. Ce comportement affecte à la fois Facebook et RenRen avec une sévérité variable :22 % des applications Facebook testées transmettent au moins un attribut à une entité externe. Quant à, RenRen, nous démontrons qu'il souffre d'une faille majeure causée par la fuite du jeton d'accès dans 69 % des cas. / Sharing information between users constitutes the cornerstone of the Web 2.0. Online Social Networks (OSN), with their billions of users, are a core component of this new generation of the web. In fact, OSNs offer innovative services allowing users to share their self-generated content (e.g., status, photos etc.) for free. However, this free access is usually synonymous with a subtle counterpart: the collection and usage of users' personal information in targeted advertisement. To achieve this goal, OSN providers are collecting a tremendous amount of personal, and usually sensitive, information about their users. This raises concerns as this data can be exploited by several entities to breach user privacy. The primary research goals of this thesis are directed toward understanding the privacy impact of OSNs.Our first contribution consists in demonstrating the privacy threats behind releasing personal information publicly. Two attacks are constructed to show that a malicious attacker (i.e., any external attacker with access to the public profile) can breach user privacy and even threaten his online security.Our first attack shows how seemingly harmless interests (e.g., music interests) can leak privacy-sensitive information about users. In particular, we infer their undisclosed (private) attributes using the public attributes of other users sharing similar interests. Leveraging semantic knowledge from Wikipedia and a statistical learning method, we demonstrated through experiments ---based on more than 104K Facebook profiles--- that our inference technique efficiently predicts attributes that are very often hidden by users.Our second attack is at the intersection of computer security and privacy. In fact, we show the disastrous consequence of privacy breach on security by exploiting user personal information ---gathered from his public profile--- to improve the password cracking process.First, we propose a Markov chain password cracker and show through extensive experiments that it outperforms all probabilistic password crackers we compared against. In a second step, we systematically analyze the idea that additional personal information about a user helps in speeding up password guessing. We propose a methodology that exploits this information in the cracking process and demonstrate that the gain can go up to 30%.These studies clearly indicate that publicly disclosing personal information harms privacy, which calls for a method to estimate this loss. Our second contribution tries to answer this question by providing a quantitative measure of privacy. We propose a practical, yet formally proved, method to estimate the uniqueness of each profile by studying the amount of information carried by public profile attributes. To achieve our goal, we leverage Ads Audience Estimation platform and an unbiased sample of more than 400K Facebook public profiles. Our measurement results show that the combination of gender, current city and age can identify close to 55% of users to within a group of 20 and uniquely identify around 18% of them.In the second part of this thesis, we investigate the privacy threats resulting from the interactions between the OSN platform and external entities. First, we explore the tracking capabilities of the three major OSNs (i.e., Facebook, Google+ and Twitter) and show that ``share-buttons'' enable them to persistently and accurately track users' web activity. Our findings indicate that OSN tracking is diffused among almost all website categories which allows OSNs to reconstruct a significant portion of users' web profile and browsing history.Finally, we develop a measurement platform to study the interaction between OSN applications --- of Facebook and RenRen --- and fourth parties. We show that several third party applications are leaking user information to ``fourth'' party entities such as trackers and advertisers. This behavior affects both Facebook and RenRen with varying severity.