Sharing and inappropriate discarding of syringes and drug use equipment can lead to transmission of bloodborne pathogens and decreased sense of community safety. To reduce these risks, interventions such as syringe drop boxes, are implemented. However, little consideration has been made of the social and spatial networks of the injection drug use (IDU) populations in the placement of these drop boxes. A sample of IDU was obtained through respondent driven sampling in Winnipeg, Canada in 2009. Characteristics of the sample and distribution of these characteristics through the social network were assessed. A spatial network was constructed which focused on the connections between IDU and specific geographic locations. Measures of centrality were calculated using Pajek and the geographic network was mapped using ArcGIS. Analysis of the social network revealed variation among network components in demographic and drug use characteristics. Spatial analysis revealed geographic clustering, quantified through network centrality measures. There was congruence between locations of high degree and current drop box placement in Winnipeg. This research illustrates the benefit of combining IDU social network and spatial data to inform evidence-based municipal policies and programs.

Internet tem sido uma importante fonte para as pessoas que buscam informações de saúde. Isto é particularmente problemático na perspectiva da Web 2.0. A Web 2.0 é a segunda geração da World Wide Web, onde os usuários interagem e colaboram uns com os outros como criadores de conteúdo. A falta de qualidade das informações médicas na Web 2.0 tem suscitado preocupações com os impactos prejudiciais que podem acarretar. São muitos os aspectos relacionados à qualidade da informação que devem ser investigados, como por exemplo, existe alguma evidência de que o autor tem alguma autoridade no domínio da saúde? Há indícios de que os autores são tendenciosos? Como saber se a fonte de informação tem reputação, como separar as fontes de boa qualidade das outras? Esses questionamentos se tornam mais evidentes quando se faz buscas no Twitter. O usuário precisa por si só selecionar o conteúdo que acredita que tenha qualidade entre as centenas de resultados. Nesse contexto, o principal objetivo deste trabalho é propor e avaliar uma abordagem que permita estimar a reputação de fontes de informação no domínio da saúde. Acredita-se que discussões sobre reputação só fazem sentido quando possuem um propósito e estão inseridas em um contexto. Sendo assim, considera-se que reputação é um atributo que um usuário se apropria quando a informação que ele divulga é crível e digna de confiança. As contribuições desta tese incluem uma nova metodologia para estimar a reputação e uma estrutura topológica de rede baseada no grau de interação entre atores sociais. O estudo permitiu compreender como as métricas afetam o ordenamento da reputação. Escolher a métrica mais apropriada depende basicamente daquilo que se quer representar. No nosso caso, o Pagerank funcionou como um “contador de arcos” representando apenas uma medida de popularidade daquele nó. Verificou-se que popularidade (ou uma posição de destaque na rede) não necessariamente se traduz em reputação no domínio médico. Os resultados obtidos evidenciaram que a metodologia de ordenamento e a topologia da rede obtiveram sucesso em estimar a reputação. Além disso, foi verificado que o ambiente Twitter desempenha um papel importante na transmissão da informação e a “cultura” de encaminhar uma mensagem permitiu inferir processos de credibilidade e consequentemente a reputação. / The Internet is an important source for people who are seeking healthcare information. This is particularly problematic in era of Web 2.0. The Web 2.0 is a second generation of World Wide Web, where users interact and collaborate with each other as creators of content. Many concerns have arisen about the poor quality of health-care information on the Web 2.0, and the possibility that it leads to detrimental effects. There are many issues related to information quality that users continuously have to ask, for example, is there any evidence that the author has some authority in health domain? Are there clues that the authors are biased? How shall we know what our sources are worth, how shall we be able to separate the bad sources from the good ones? These questions become more obvious when searching for content in Twitter. The user then needs to manually pick out high quality content among potentially thousands of results. In this context, the main goal of this work is to propose an approach to infer the reputation of source information in the medical domain. We take into account that, discussion of reputation is usually not meaningful without a specific purpose and context. Thus, reputation is an attribute that a user comprises, and the information disseminated by him is credible and worthy of belief. Our contributions were to provide a new methodology to Rank Reputation and a new network topological structure based on weighted social interaction. The study gives us a clear understanding of how measures can affect the reputation rank. Choosing the most appropriate measure depends on what we want to represent. In our case, the PageRank operates look alike “edges counts” as the “popularity” measures. We noticed that popularity (or key position in a graph) does not necessarily refer to reputation in medical domain. The results shown that our rank methodology and the network topology have succeeded in achieving user reputation. Additionally, we verified that in Twitter community, trust plays an important role in spreading information; the culture of “retweeting” allowed us to infer trust and consequently reputation.

Internet tem sido uma importante fonte para as pessoas que buscam informações de saúde. Isto é particularmente problemático na perspectiva da Web 2.0. A Web 2.0 é a segunda geração da World Wide Web, onde os usuários interagem e colaboram uns com os outros como criadores de conteúdo. A falta de qualidade das informações médicas na Web 2.0 tem suscitado preocupações com os impactos prejudiciais que podem acarretar. São muitos os aspectos relacionados à qualidade da informação que devem ser investigados, como por exemplo, existe alguma evidência de que o autor tem alguma autoridade no domínio da saúde? Há indícios de que os autores são tendenciosos? Como saber se a fonte de informação tem reputação, como separar as fontes de boa qualidade das outras? Esses questionamentos se tornam mais evidentes quando se faz buscas no Twitter. O usuário precisa por si só selecionar o conteúdo que acredita que tenha qualidade entre as centenas de resultados. Nesse contexto, o principal objetivo deste trabalho é propor e avaliar uma abordagem que permita estimar a reputação de fontes de informação no domínio da saúde. Acredita-se que discussões sobre reputação só fazem sentido quando possuem um propósito e estão inseridas em um contexto. Sendo assim, considera-se que reputação é um atributo que um usuário se apropria quando a informação que ele divulga é crível e digna de confiança. As contribuições desta tese incluem uma nova metodologia para estimar a reputação e uma estrutura topológica de rede baseada no grau de interação entre atores sociais. O estudo permitiu compreender como as métricas afetam o ordenamento da reputação. Escolher a métrica mais apropriada depende basicamente daquilo que se quer representar. No nosso caso, o Pagerank funcionou como um “contador de arcos” representando apenas uma medida de popularidade daquele nó. Verificou-se que popularidade (ou uma posição de destaque na rede) não necessariamente se traduz em reputação no domínio médico. Os resultados obtidos evidenciaram que a metodologia de ordenamento e a topologia da rede obtiveram sucesso em estimar a reputação. Além disso, foi verificado que o ambiente Twitter desempenha um papel importante na transmissão da informação e a “cultura” de encaminhar uma mensagem permitiu inferir processos de credibilidade e consequentemente a reputação. / The Internet is an important source for people who are seeking healthcare information. This is particularly problematic in era of Web 2.0. The Web 2.0 is a second generation of World Wide Web, where users interact and collaborate with each other as creators of content. Many concerns have arisen about the poor quality of health-care information on the Web 2.0, and the possibility that it leads to detrimental effects. There are many issues related to information quality that users continuously have to ask, for example, is there any evidence that the author has some authority in health domain? Are there clues that the authors are biased? How shall we know what our sources are worth, how shall we be able to separate the bad sources from the good ones? These questions become more obvious when searching for content in Twitter. The user then needs to manually pick out high quality content among potentially thousands of results. In this context, the main goal of this work is to propose an approach to infer the reputation of source information in the medical domain. We take into account that, discussion of reputation is usually not meaningful without a specific purpose and context. Thus, reputation is an attribute that a user comprises, and the information disseminated by him is credible and worthy of belief. Our contributions were to provide a new methodology to Rank Reputation and a new network topological structure based on weighted social interaction. The study gives us a clear understanding of how measures can affect the reputation rank. Choosing the most appropriate measure depends on what we want to represent. In our case, the PageRank operates look alike “edges counts” as the “popularity” measures. We noticed that popularity (or key position in a graph) does not necessarily refer to reputation in medical domain. The results shown that our rank methodology and the network topology have succeeded in achieving user reputation. Additionally, we verified that in Twitter community, trust plays an important role in spreading information; the culture of “retweeting” allowed us to infer trust and consequently reputation.

Internet tem sido uma importante fonte para as pessoas que buscam informações de saúde. Isto é particularmente problemático na perspectiva da Web 2.0. A Web 2.0 é a segunda geração da World Wide Web, onde os usuários interagem e colaboram uns com os outros como criadores de conteúdo. A falta de qualidade das informações médicas na Web 2.0 tem suscitado preocupações com os impactos prejudiciais que podem acarretar. São muitos os aspectos relacionados à qualidade da informação que devem ser investigados, como por exemplo, existe alguma evidência de que o autor tem alguma autoridade no domínio da saúde? Há indícios de que os autores são tendenciosos? Como saber se a fonte de informação tem reputação, como separar as fontes de boa qualidade das outras? Esses questionamentos se tornam mais evidentes quando se faz buscas no Twitter. O usuário precisa por si só selecionar o conteúdo que acredita que tenha qualidade entre as centenas de resultados. Nesse contexto, o principal objetivo deste trabalho é propor e avaliar uma abordagem que permita estimar a reputação de fontes de informação no domínio da saúde. Acredita-se que discussões sobre reputação só fazem sentido quando possuem um propósito e estão inseridas em um contexto. Sendo assim, considera-se que reputação é um atributo que um usuário se apropria quando a informação que ele divulga é crível e digna de confiança. As contribuições desta tese incluem uma nova metodologia para estimar a reputação e uma estrutura topológica de rede baseada no grau de interação entre atores sociais. O estudo permitiu compreender como as métricas afetam o ordenamento da reputação. Escolher a métrica mais apropriada depende basicamente daquilo que se quer representar. No nosso caso, o Pagerank funcionou como um “contador de arcos” representando apenas uma medida de popularidade daquele nó. Verificou-se que popularidade (ou uma posição de destaque na rede) não necessariamente se traduz em reputação no domínio médico. Os resultados obtidos evidenciaram que a metodologia de ordenamento e a topologia da rede obtiveram sucesso em estimar a reputação. Além disso, foi verificado que o ambiente Twitter desempenha um papel importante na transmissão da informação e a “cultura” de encaminhar uma mensagem permitiu inferir processos de credibilidade e consequentemente a reputação. / The Internet is an important source for people who are seeking healthcare information. This is particularly problematic in era of Web 2.0. The Web 2.0 is a second generation of World Wide Web, where users interact and collaborate with each other as creators of content. Many concerns have arisen about the poor quality of health-care information on the Web 2.0, and the possibility that it leads to detrimental effects. There are many issues related to information quality that users continuously have to ask, for example, is there any evidence that the author has some authority in health domain? Are there clues that the authors are biased? How shall we know what our sources are worth, how shall we be able to separate the bad sources from the good ones? These questions become more obvious when searching for content in Twitter. The user then needs to manually pick out high quality content among potentially thousands of results. In this context, the main goal of this work is to propose an approach to infer the reputation of source information in the medical domain. We take into account that, discussion of reputation is usually not meaningful without a specific purpose and context. Thus, reputation is an attribute that a user comprises, and the information disseminated by him is credible and worthy of belief. Our contributions were to provide a new methodology to Rank Reputation and a new network topological structure based on weighted social interaction. The study gives us a clear understanding of how measures can affect the reputation rank. Choosing the most appropriate measure depends on what we want to represent. In our case, the PageRank operates look alike “edges counts” as the “popularity” measures. We noticed that popularity (or key position in a graph) does not necessarily refer to reputation in medical domain. The results shown that our rank methodology and the network topology have succeeded in achieving user reputation. Additionally, we verified that in Twitter community, trust plays an important role in spreading information; the culture of “retweeting” allowed us to infer trust and consequently reputation.

Sharing and inappropriate discarding of syringes and drug use equipment can lead to transmission of bloodborne pathogens and decreased sense of community safety. To reduce these risks, interventions such as syringe drop boxes, are implemented. However, little consideration has been made of the social and spatial networks of the injection drug use (IDU) populations in the placement of these drop boxes. A sample of IDU was obtained through respondent driven sampling in Winnipeg, Canada in 2009. Characteristics of the sample and distribution of these characteristics through the social network were assessed. A spatial network was constructed which focused on the connections between IDU and specific geographic locations. Measures of centrality were calculated using Pajek and the geographic network was mapped using ArcGIS. Analysis of the social network revealed variation among network components in demographic and drug use characteristics. Spatial analysis revealed geographic clustering, quantified through network centrality measures. There was congruence between locations of high degree and current drop box placement in Winnipeg. This research illustrates the benefit of combining IDU social network and spatial data to inform evidence-based municipal policies and programs.

The goal of this thesis is to map the educational environment and trends relevant for the implementation of internal social networks at Czech universities. Based on these findings to deliver viable business plan for launching internal social networks at Czech public universities. First part deals with the theoretical background of the business plan formation as well as its usage, forms and structure. Furthermore, it portrays IT technology currently used by universities and describes implementation and adoption of internal social networks. Second part focuses on the elaboration of a specific business plan for the company Hungry GECKO with focus on providing university social network. The whole business plan is based on the executed market research and quantified in the financial plan.

Previous research has mainly focused on the social capital formation process on Facebook. In general, professionally-oriented social network sites (P-SNSs), such as LinkedIn, are under-researched in the Information Systems discipline. In addition, current studies do not include the effects of important elements of social network sites (SNS) such as one’s profile on social capital formation. As such, the main objective of this research is to propose and validate a model that explains the process by which individuals develop and accrue social capital through using P-SNSs. The theoretical framework of the proposed research draws upon Social Network Analysis, Social Media Analysis, and Social Capital Theory. Using an online survey of 377 LinkedIn users, this study finds that: (1) P-SNS users’ actions (perceived profile disclosure, active participation, and passive consumption) have significant positive effects on perceived social connectedness; (2) perceived social connectedness on P-SNSs has a significant positive effect on perceived networking value on these sites; (3) perceived profile disclosure and passive consumption have significant positive effects on network size; (4) active participation does not have any effect on network size; and (5) network size does not have a significant effect on perceived networking value. Overall, this investigation advances our understanding of how social capital is formed in P-SNSs. Additionally, by including the profile disclosure construct in the research model, this is the first study in the P-SNS context that investigates the role of the user profile in the social capital formation process, along with user actions such as active participation and passive consumption. From a practical perspective, this study has implications for different audiences such as job seekers, policy-makers, and P-SNS providers, assisting them in playing a more effective role in the social capital formation process on P-SNSs. / Thesis / Doctor of Business Administration (DBA) / In recent years, people increasingly spend their time on various social network sites (SNSs) such as Facebook and LinkedIn. This raises a serious question as to how people gain actual benefits from using these sites. This research examines this question from the lens of social capital. As such, the main objective of this research was to propose and validate a model that explains the process by which individuals develop social capital through professionally-oriented SNS such as LinkedIn. This study finds that to gain actual benefits from professionally-oriented SNS, such as networking value, people need to feel connected to their social networks on the site. This feeling of connection requires that people actively participate on the site (e.g., share a post) rather than just reading and following other people’s posts. Also, to connect with more people, individuals should disclose more information on the site.

Cette thèse utilise la théorie des jeux pour comprendre le comportement des usagers dans les réseaux sociaux. Trois problématiques y sont abordées: "Comment maximiser la popularité des contenus postés dans les réseaux sociaux?";" Comment modéliser la répartition des messages par sujets?";"Comment minimiser la propagation d’une rumeur et maximiser la diversité des contenus postés?". Après un état de l’art concernant ces questions développé dans le chapitre 1, ce travail traite, dans le chapitre 2, de la manière d’aborder l’environnement compétitif pour accroître la visibilité. Dans le chapitre 3, c’est le comportement des usagers qui est modélisé, en terme de nombre de messages postés, en utilisant la théorie des approximations stochastiques. Dans le chapitre 4, c’est une compétition pour être populaire qui est étudiée. Le chapitre 5 propose de formuler deux problèmes d’optimisation convexes dans le contexte des réseaux sociaux en ligne. Finalement, le chapitre 6 conclue ce manuscrit. / This Ph.D. is dedicated to the application of the game theory for the understanding of users behaviour in Online Social Networks. The three main questions of this Ph.D. are: " How to maximize contents popularity ? "; " How to model the distribution of messages across sources and topics in OSNs ? "; " How to minimize gossip propagation and how to maximize contents diversity? ". After a survey concerning the research made about the previous problematics in chapter 1, we propose to study a competition over visibility in chapter 2. In chapter 3, we model and provide insight concerning the posting behaviour of publishers in OSNs by using the stochastic approximation framework. In chapter 4, it is a popularity competition which is described by using a differential game formulation. The chapter 5 is dedicated to the formulation of two convex optimization problems in the context of Online Social Networks. Finally conclusions and perspectives are given in chapter 6.

An individual’s alcohol use influences, and is influenced by, the drinking in their social network. Indeed, a person’s propensity to drink heavily is associated with having a social network in which more people drink heavily. This association has largely been reported in non-clinical samples of young adults and in networks of 10 or fewer relationships. Exploring social networks in older adults and in more distal relationships may further elucidate the interplay between an individual’s drinking and their social network. This dissertation explores the associations between a person’s social network and their drinking behaviour in adults, in clinical samples of people with alcohol use disorder (AUD), and in both proximal and distal social networks. The first study explores the psychometric properties of a brief egocentric (i.e., individual self-reports on their network) social network analysis (SNA) measure in general community adult drinkers. The second study compares a brief and a more extensive, or formal, SNA measure in terms of the capacity to identify AUD. The third study investigates the association between social networks and recovery from AUD, using a simple measure of social network alcohol use. Finally, the fourth study examines whether a higher-resolution SNA tool could provide a more in-depth understanding of the relationship between social networks and recovery from AUD. The results of this dissertation validate several uses of SNA in the context of AUD in adults: first, a brief measure demonstrates excellent psychometric properties. Second, both brief and formal SNA measures demonstrate the capacity to accurately classify those with and without AUD. Third, social networks play both a moderating and/or mediating role in AUD recovery, depending on the phase of recovery and type of social network measure used. Collectively, this dissertation provides an important foundation for further applications of SNA in clinical research and practice. / Thesis / Doctor of Philosophy (PhD) / An individual's social network plays a critical role in influencing behaviour, including their alcohol use. However, the majority of research investigating this association has focused on young adults in non-clinical samples and in close social circles. The purpose of this dissertation was to explore the role of an individual’s social networks on that individual’s drinking behaviour across a broad age range, in clinical samples, and in both immediate and extended social circles. The results suggest that social network alcohol use is consistently associated with an individual’s alcohol consumption. Beyond providing important insight into the social context of alcohol use, the findings indicate that measuring network influences could provide clinical value by potentially informing diagnostic assessments of alcohol use disorder and intervention approaches that focus on both the individual and their social context.

Social support is a key factor influencing older adults’ health and well-being. Disclosing one’s lesbian, gay, or bisexual identity at any age has great potential for altering, if not destroying, existing relationships with family, friends, and others. With long-established social roles and personal relationships, the potential risks may be accentuated for those who come out in mid- or later-life. Yet, researchers have paid scant attention to this phenomenon. This exploratory qualitative study examines the impact of coming out “late” on older adults’ social networks. In-depth interviews were conducted with a sample of fourteen older adults who disclosed their non-heterosexual identity at or after age 39. Interviews inquired about participants’ past and present social networks and the coming out process, particularly the influence of coming out “off time.” Findings show coming out is a dynamic, continuous, and non-linear process that simultaneously characterizes and is characterized by social network gains and losses.