A presente pesquisa foi elaborada a partir de um projeto prático, realizado no período entre 2010 e 2012. Desse modo, a reflexão que segue foi desenvolvida a partir dos procedimentos de trabalho, procurando identificar as recorrências, as referências, as escolhas, e suas consequentes implicações conceituais. O objeto de pesquisa compreende a troca de desejos por desenhos, feita através da internet e usando as redes sociais. Buscou-se aprofundar os importantes aspectos relacionais presentes nessa dinâmica, bem como aqueles relativos ao meio em que as trocas foram realizadas. A instauração e a articulação de três coleções distintas objetivou organizar os documentos de trabalho e as questões que concernem a apropriação, refletindo sobre como essas práticas se articulam e contribuem para a ampliação das possibilidades do trabalho criativo. / The herein presented research was elaborated based upon the practical work executed between the years of 2010 and 2012. The following reflection was developed from work procedures, trying to identify the recurrences, the references, the choices and its resulting conceptual implications. The object of research includes the exchange of desires for drawings, made through the internet and social networks. It searched to deepen the important relational aspects present in this dynamic, as well as aspects relating to the environment in which exchanges were made. The establishment and articulation of three distinct collections aimed to organize the working documents and the issues that concern appropriation, reflecting on how these practices work together and contribute to the expansion of creative possibilities.

Master of Public Health / Department of Kinesiology / Brandon Irwin / BACKGROUND: Resource-deficient social networks can have detrimental health effects on individuals. In part, this may result from limited engagement in health-related behaviors such as physical activity (PA). PA can lower one’s risk for a variety of chronic diseases and promoting this behavior is a public health priority. Understanding the relationship between network-owned resources and PA behavior can help policymakers and health advocates develop comprehensive programs to promote health among those who are disadvantaged. Measurements of resources available through an individual's social network have previously been referred to as social capital (SC). This study set out to explore the relationship between SC and PA by testing the following hypotheses: (1) Individuals with low household income will report low levels of both SC and PA participation, (2) SC has a positive relationship with PA participation, (3) Individuals who engage in leisure-time PA with others will report higher levels of SC than those who do it alone, (4) Social support for PA mediates the relationship between SC and PA. METHODS: Data were obtained from residents of Riley County, KS (n=828) who completed a survey. One-way analyses of variance (ANOVA) were used to examine differences in SC (bonding, bridging, total) and PA (moderate, vigorous, total, leisure-time) between different income groups. Relationships between SC and PA variables were examined using Pearson product-moment correlations, while independent sample t-tests were used to identify differences in SC between those who did or did not meet recommended PA guidelines. ANOVAs were also used to identify differences in SC among those who primarily engage in leisure-time PA by themselves, with a partner, or with a group. Finally, the Sobel test was used to examine the mediating effect of social support for PA in all significant linear relationships between SC and PA variables. RESULTS: Social capital was highest among high-income groups (p <.05). Leisure-time PA was lowest in the poorest income group (p <.05). Weak linear relationships were identified between bonding SC and leisure-time PA [r(786) = 0.09, p = 0.01], as well as between total SC and leisure time PA [r(775) = 0.08, p = 0.02]. Individuals who met PA guidelines reported higher levels of SC than those who did not (bridging and total: p <.001; bonding: p = <.01). No differences in SC were found between individuals who primarily engaged in leisure-time PA by themselves, with a partner, or with a group. The Sobel test found that social support for PA fully mediated the relationships between leisure-time PA and both bonding SC (z = 5.61, p <.001) and total SC (z = 6.01, p = <.001). CONCLUSIONS: Social capital has a positive relationship with leisure-time physical activity and influences this behavior through provisions of behavior-targeted social support. Further examination should be afforded to the relationship between social capital, income, and leisure-time physical activity. While physical activity promotion efforts that target social capital may achieve marginal improvements in physical activity levels at the population level, they are unlikely to result in significant improvements at the individual level.

The popularity of location empowered devices such as GPS enabled smart-phones has immensely amplified the use of location-based services in social networks. This happened by allowing users to share Geo-tagged contents such as current locations/check-ins with their social network friends. These location-aware social networks are called Location-based Social Networks (LBSN), and examples include Foursquare and Gowalla. The data of LBSNs are being used for providing different kinds of services such as the recommendation of locations, friends, activities, and media contents, and the prediction of user's locations. To provide such services, different queries are utilized that exploit activity/check-in data of users. Usually, LBSN data is divided into two parts, a social graph that encapsulates the friendships of users and an activity graph that maintains the visit history of users at locations. Such a data separation is scalable enough for processing queries that directly utilize friendship information and visit history of users. These queries are called user and activity analytic queries. The visits of users at locations create relationships between those locations. Such relationships can be built on different features such as common visitors, geographical distance, and mutual location categories between them. The process of analysing such relationships for optimizing location-based services is termed Location Analytics. In location analytics, we expose the subjective nature of locations that can further be used for applications in the domain of prediction of visitors, traffic management, route planning, and targeted marketing.In this thesis, we provide a general LBSN data model which can support storage and processing of queries required for different applications, called location analytics queries. The LBSN data model we introduce, segregates the LBSN data into three graphs: the social graph, the activity graph, and the location graph. The location graph maintains the interactions of locations among each other. We define primitive queries for each of these graphs. In order to process an advanced query, we express it as a combination of these primitive queries and process them on corresponding graphs in parallel. We further provide a distributed data processing framework called GeoSocial-GraphX (GSG). GSG implements the aforementioned LBSN data model for efficient and scalable processing of the queries. We further exploit the location graph for providing novel location analytics queries in the domain of influence maximization and visitor prediction. We introduce a notion of location influence. Such influence can capture the interactions of locations based on their visitors and can be used for propagation of information between them. The applications of such a query lie in the domain of outdoor marketing, and simulation of virus and news propagation. We also provide a unified system IMaxer that can evaluate and compare different information propagation mechanisms. We further exploit the subjective nature of locations by analysing the mobility behaviour of their visitors. We use such information to predict the individual visitors as well as the groups of visitors (cohorts) in future for those locations. The prediction of visitors can be used for better event planning, traffic management, targeted marketing, and ride-sharing services.In order to evaluate the proposed frameworks and approaches, we utilize data from four real-life LBSNs: Foursquare, Brightkite, Gowalla, and Wee Places. The detailed LBSN data mining and statistically significant experimental evaluation results show the effectiveness, efficiency, and scalability of our proposed methods. Our proposed approaches can be employed in real systems for providing life-care services. / Doctorat en Sciences de l'ingénieur et technologie / The portal is not showing my complete name. The name (my complete name), I want to have on the diploma is "Muhammad Aamir Saleem". Please correct this issue. / info:eu-repo/semantics/nonPublished

From 1976 to 2000, an estimated three quarters of a million Jewish refugees from the former Soviet Union immigrated to the United States. These refugees were welcomed by both volunteers and professional aid workers from the American Jewish community who provided food, shelter, and a helping hand in establishing a new life in a new place. Social capital accumulated through membership in a global Jewish identity, both for Soviet and American Jews, provided the foundation for this aid. The shift in identity from #8220;American#8221; or #8220;Russian#8221; to #8220;Jewish & rdquol that provided the initial transnational social capital was largely the result of the efforts of the Soviet Jewish Freedom Movement, centered in Cleveland and New York City. Additionally, the descendants of Soviet Jewish refugees appear to be assimilating with native-born populations. Through interviews with Soviet Jewish refugees and other key participants, this dissertation examines the role of place in the shifting identities of Soviet Jewish refugees living in Cleveland. From the evidence gathered through this case study and building on the work of Bourdieu and Lefebvre, this dissertation culminates in the development of a new model of Scalar Assimilation that allows for identity shifts and assimilation processes to simultaneously operate at multiple scales with a variety of outcomes.

Orientador: Ricardo Cesar Gonçalves Sant'Ana / Banca: Silvana Aparecida Borsetti Gregório Vidotti / Banca: Guilherme Ataíde Dias / Banca: Ana Alice Baptista Rodrigues / Banca: José Vicente Rodríguez Muñoz / Resumo: O desenvolvimento das redes sociais é tema de estudos de várias áreas do conhecimento, e com o aumento do uso da Internet em atividades profissionais e de entretenimento, surgiram as redes sociais online: serviços com o intuito de proporcionar uma interface de relacionamento entre indivíduos. Algumas destas redes possuem milhões de usuários, que consentiram acordo aos Termos de Uso. Os Termos de Uso destes serviços contém a delimitação dos processos de coleta de dados por agentes externos, criando um efeito em cascata de identificação do usuário, e pode potencializar atividades prejudiciais à privacidade. O estudo procura verificar se processos sistematizados de coleta de dados sobre documentos que contém características das interfaces de coleta das Application Programming Interfaces (APIs), e os Termos de Uso podem auxiliar a identificação de atividades potencialmente prejudiciais à privacidade dos usuários (referenciados) e revelar pré-requisitos de conhecimentos sobre as tecnologias envolvidas neste processo, conceitos prévios à identificação das características, e áreas profissionais envolvidas no entendimento das informações sobre tecnologias voltadas às APIs e condições dos Termos de Uso. O objetivo é propor um modelo de dados orientado a análise sobre questões de privacidade de dados pessoais, a partir da identificação das características da coleta de dados de referenciados via API, para auxiliar na identificação de potenciais ações e atividades prejudiciais à privacid... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The development of social networks is a topic of study for several areas, and with the increased use of the Internet in professional and leisure activities, online social networks have emerged: services with the goal of providing an interface between individuals. Some of these networks have millions of users, who agree and give their consente to the Terms of Use. The Terms of Use of these services contain the delimitation of the processes of data collection by external agents, creating a cascading effect of user identification and can enhance activities which are detrimental to user privacy. This study looks to verify if the systematic data collection processes for documents which contain characteristics of the Application Programming Interfaces (APIs) data collection and the Terms of Use can help in identifying activities potentially harmful to user privacy (referenced) and reveal prerequisites of knowledge about the technology involved in this process, concepts prior to identifying characteristics and professional areas involved in understanding the technology of the API and the Terms of Use. The objective is to propose an analysis based data model on personal privacy data issues, from the identification of the characteristics of the collection of data from the referenced API to assisting in identifying potential actions and activities which are detrimental to privacy obtained through the data collection process. The research universe is limited to the services available on the Internet that use APIs as interoperability interfaces of their content and the sample was defined in three APIs: from Facebook, Twitter and LinkedIn. The methodology adopted was exploratory analysis, in qualitative form, with combined methods based on the exploitation of the technical characteristics of APIs and the reading of available documents, being segmented by the perspectives: professional ... / Doutor

La presente investigación presenta la problemática dado por los avances tecnológicos relacionados al uso de redes sociales en Internet, que ocasionan que sus relaciones interpersonales que pueden ser consideradas factores potenciadores de conductas psicológicas, que causan trastornos de dependencia, hasta problemas de adicción.Esta investigación tiene como objetivo establecer la relación entre el manejo interpersonal y la adicción a redes sociales en los estudiantes de psicología de una universidad privada de Lima. The present investigation presents the problems given by the technological advances related to the use of social networks on the Internet, which cause their interpersonal relationships that can be considered Psychological behavior-enhancing factors, which cause dependency disorders, to problems of This investigation aims to establish the relationship between interpersonal management and the addiction to social networks in the psychology students of a private university in Lima.

CARNEIRO, Jéssica de Souza. Fotografia e memória autobiográfica no Facebook: narrativas de si mediadas pela imagem. 2016. 145f. – Dissertação (Mestrado) – Universidade Federal do Ceará, Programa de Pós-graduação em Psicologia, Fortaleza (CE), 2016. / Submitted by Márcia Araújo (marcia_m_bezerra@yahoo.com.br) on 2016-06-15T12:31:26Z No. of bitstreams: 1 2016_dis_jscarneiro.pdf: 4095784 bytes, checksum: 037e9fe65c176481d5f3a10d0008e249 (MD5) / Approved for entry into archive by Márcia Araújo (marcia_m_bezerra@yahoo.com.br) on 2016-06-15T13:14:34Z (GMT) No. of bitstreams: 1 2016_dis_jscarneiro.pdf: 4095784 bytes, checksum: 037e9fe65c176481d5f3a10d0008e249 (MD5) / Made available in DSpace on 2016-06-15T13:14:34Z (GMT). No. of bitstreams: 1 2016_dis_jscarneiro.pdf: 4095784 bytes, checksum: 037e9fe65c176481d5f3a10d0008e249 (MD5) Previous issue date: 2016 / The study of memory and narrative and its relation with the production and consumption of images has shown a challenging course, especially nowadays, when we try to understand these processes in digital environments. The photo operates in the processes of meaning attribution, that is, the meanings that subjects give to a certain event, in how people and events are remembered, narrated and understood. In particular, the digital photograph produced in order to be shared online introduces an interesting field of research of both and autobiographical memory and narration. This study proposes to analyze the photos shared on Facebook that play on the autobiographical memories, with effect therefore on current forms of self-narration. In other words, we try to understand how the new digital ways of producing images (especially those produced to be shared) operate in the way users build their memories of a lived moment, articulating the past, present and future from the mediatized images. In addition, we investigate how these new ways of "saving" memory influence the creation of current autobiographical narratives, that is, the stories that people tell about themselves. The qualitative study consisted in two stages: 1) application of semi-structured questionnaire with topics about photography and memory; 2) coding and analysis of the photographs available for research, selected by respondents. The invitation to participate was given on Facebook. We identified the factors that contributed to the process of choosing the shared images, as well as the individual and social processes that circumscribe the practices of production of digital photography sharing, with the intention of establishing a relationship between the production of memory and self-narratives. / O estudo da memória e da narrativa e de sua relação com a produção e o consumo de imagens mostra-se um caminho desafiador, sobretudo na contemporaneidade, quando tentamos compreender esses processos nos ambientes digitais. A fotografia opera nos processos de significação, isto é, nos significados que os sujeitos conferem a um dado acontecimento, no modo como eventos e pessoas são lembrados, contados e compreendidos. Em especial, a fotografia digital produzida para ser compartilhada online introduz um campo de problemas interessantes para investigação dos processos de memória e narração autobiográficas. Este estudo propõe analisar como as fotografias compartilhadas no Facebook atuam sobre a produção de memórias autobiográficas, com efeitos, portanto, sobre atuais formas de narração de si. Em outras palavras, procuramos entender como as novas formas digitais de produzir imagens (principalmente aquelas produzidas para serem compartilhadas) operam na forma como os usuários constroem suas lembranças do vivido, articulando as relações entre passado, presente e futuro a partir das imagens midiatizadas. Além disso, investigamos como essas novas maneiras de “guardar” a memória atuam sobre a criação das atuais narrativas autobiográficas, isto é, as histórias que as pessoas contam sobre si mesmas. Esta pesquisa apoia-se nos estudos de memória, narrativa e novas mídias de Van Dijck , Brockmeier e Sibilia, e em Dubois e Samain no que se refere à natureza, ao funcionamento e à análise da fotografia. O estudo qualitativo consistiu em duas etapas: 1) aplicação do questionário semi-estruturado com temas sobre fotografia e memória; 2) codificação e análise das fotografias disponibilizadas para a pesquisa, selecionadas pelos respondentes. O convite à participação se deu no próprio Facebook. Identificamos os fatores que contribuem para o processo de escolha da imagem compartilhada e os processos individuais e sociais que circunscrevem as práticas de produção e compartilhamento da fotografia digital, procurando estabelecer uma relação entre a produção de memória e as autonarrativas.

Recentemente, as medidas de centralidade ganharam relevância nas pesquisas com redes complexas e redes sociais, atuando como preditores comportamentais, na identificação de elementos de poder e influência, na detecção de pontos estratégicos para a comunicação e para a transmissão de doenças. Novas métricas foram criadas e outras reformuladas, mas pouco tem sido feito para que se entenda a relação existente entre as diferentes medidas de centralidades, assim como sua relação com outras propriedades estruturais das redes em que elas são frequentemente aplicadas. Nossa pesquisa visa analisar e estudar essas relações para que sirvam de guia na aplicação das medidas de centralidade existentes em novos contextos e aplicações. Nós apresentamos também evidencias que indicam um desempenho superior das medidas conhecidas como Walk Betweenness, Information, Eigenvector and Betweenness na distinção de vértices das redes somente pelas suas características estruturais. Ainda, nós propiciamos detalhes sobre o desempenho distinto de cada métrica de acordo com o tipo de rede em que se trabalha. Adicionalmente, mostramos que várias das medidas de centralidade apresentam um alto nível de redundância e concordância entre si (com correlação superior a 0,8). Um forte indício que o uso simultâneo de várias métricas é improdutivo ou pouco eficaz. Os resultados da nossa pesquisa reforçam a ideia de que para usar apropriadamente as medidades de centralidade é de extrema importância que se saiba mais sobre o comportamento e propriedades das mesmas, fato que salientamos nessa dissertação. / Over the last years, centrality measures have gained importance within complex and social networks research, e.g., as predictors of behavior, identification of powerful and influential elements, detection of critical spots in communication networks and in transmission of diseases. New measures have been created and old ones reinvented, but few have been proposed to understand the relation among measures as well as between measures and other structural properties of the networks. Our research analyzes and studies these relations with the objective of providing a guide to the application of existing centrality measures for new environments and new purposes. We shall also present evidence that the measures known as Walk Betweenness, Information, Eigenvector and Betweenness are substantially better than other metrics in distinguishing vertices in a network by their structural properties. Furthermore, we provide evidence that each metric performs better with respect to distinct kinds of networks. In addition, we show that most metrics present a high level of redundancy (over 0.8 correlation) and its simultaneous use, in most cases, is fruitless. The results achieved in our research reinforce the idea that to use centrality measures properly, knowledge about their underlying properties and behavior is valuable, as we show in this dissertation.

Atualmente empresas como Google e Facebook fazem parte da lista das maiores companhias do mundo. O investimento em publicidade e criação de páginas para a divulgação de anúncios e marcas, tem levado o Facebook a uma posição de destaque neste cenário. Neste contexto, o presente trabalho tem o objetivo de analisar e predizer o número de interações em notícias divulgadas em cinco páginas de fãs, que se constituem nas mais acessadas da Rede Social Facebook no Brasil. Como contribuição propõem-se determinar o fator de impacto de publicações, considerando a média de três características mencionadas, o número de curtidas, o número de comentários e o número de vezes que a notícia foi compartilhada. Serão avaliados resultados da aplicação de diferentes técnicas para a classificação, além da influência de características relacionadas a palavras e termos mais frequentes, verificando qual combinação produz melhores resultados no processo de gerar um modelo de aprendizado para prever o Fator de Impacto de notícias publicadas nas páginas de fãs da Rede Social Facebook. Apresenta-se também os motivos que podem exercer influência no fator de impacto através do processo de descoberta de conhecimentos em base de dados e também fazendo uso de técnicas de processamento de linguagem natural com o objetivo de atender a expectativa do trabalho. / Currently companies as Google and Facebook are on the top of the largest companies in the world and according to news released on the website tecmundo the main reason that led to this privileged position, in particular Facebook, appears to be the result of its investments in publicity focused on mobile devices through general advertisements in its own social network. In this context the present research aims to estimate the number of news interactions published on the five most accessed fans pages of Facebook Social Network in Brazil. Are considered examples of interactions in this study the number of likes, the number of comments and also the amount of times a message was shared. As also disclose attributes that influence interactions. As a contribution is proposed the impact factor of a publication, considering the average of three mentioned interactions, the number of likes, the number of comments and also the number of times the news was shared, in order to improve the results in predicting interactions of a fan page of Facebook Social Network. In addition to analyze the results of prediction algorithms applying different techniques of text pre- processing checking which combination produces best results in generating a learning process model to foresee the impact of news published on Facebooks fan pages and exhibit reasons that may influence the impact factor through the discovering process of database knowledge, from the feeling analysis as well as making use of processing of natural language techniques in order to fulfill work expectation.

Online social and information networks, like Facebook and Twitter, exploit the influence of neighbors to achieve effective information sharing and spreading. The process that information is spread via the connected nodes in social and information networks is referred to as diffusion. In the literature, a number of diffusion models have been proposed for different applications like influential user identification and personalized recommendation. However, comprehensive studies to discover the hidden diffusion mechanisms governing the information diffusion using the data-driven paradigm are still lacking. This thesis research aims to design novel diffusion models with the structural and behaviorable dependency of neighboring nodes for representing social networks, and to develop computational algorithms to infer the diffusion models as well as the underlying diffusion mechanisms based on information cascades observed in real social networks. By incorporating structural dependency and diversity of node neighborhood into a widely used diffusion model called Independent Cascade (IC) Model, we first propose a component-based diffusion model where the influence of parent nodes is exerted via connected components. Instead of estimating the node-based diffusion probabilities as in the IC Model, component-based diffusion probabilities are estimated using an expectation maximization (EM) algorithm derived under a Bayesian framework. Also, a newly derived structural diversity measure namely dynamic effective size is proposed for quantifying the dynamic information redundancy within each parent component. The component-based diffusion model suggests that node connectivity is a good proxy to quantify how a node's activation behavior is affected by its node neighborhood. To model directly the behavioral dependency of node neighborhood, we then propose a co-activation pattern based diffusion model by integrating the latent class model into the IC Model where the co-activation patterns of parent nodes form the latent classes for each node. Both the co-activation patterns and the corresponding pattern-based diffusion probabilities are inferred using a two-level EM algorithm. As compared to the component-based diffusion model, the inferred co-activation patterns can be interpreted as the soft parent components, providing insights on how each node is influenced by its neighbors as reflected by the observed cascade data. With the motivation to discover a common set of the over-represented temporal activation patterns (motifs) characterizing the overall diffusion in a social network, we further propose a motif-based diffusion model. By considering the temporal ordering of the parent activations and the social roles estimated for each node, each temporal activation motif is represented using a Markov chain with the social roles being its states. Again, a two-level EM algorithm is proposed to infer both the temporal activation motifs and the corresponding diffusion network simultaneously. The inferred activation motifs can be interpreted as the underlying diffusion mechanisms characterizing the diffusion happening in the social network. Extensive experiments have been carried out to evaluate the performance of all the proposed diffusion models using both synthetic and real data. The results obtained and presented in the thesis demonstrate the effectiveness of the proposed models. In addition, we discuss in detail how to interpret the inferred co-activation patterns and interaction motifs as the diffusion mechanisms under the context of different real social network data sets.