Finding subgroups within social networks is important for understanding and possibly influencing the formation and evolution of online communities. This thesis addresses the problem of finding cohesive subgroups within social networks inferred from online interactions. The dissertation begins with a review of relevant literature and identifies existing methods for finding cohesive subgroups. This is followed by the introduction of the SCAN method for identifying subgroups in online interaction. The SCAN (Social Cohesion Analysis of Networks) methodology involves three steps: selecting the possible members (Select), collecting those members into possible subgroups (Collect) and choosing the cohesive subgroups over time (Choose). Social network analysis, clustering and partitioning, and similarity measurement are then used to implement each of the steps. Two further case studies are presented, one involving the TorCamp Google group and the other involving YouTube vaccination videos, to demonstrate how the methodology works in practice. Behavioural measures of Sense of Community and the Social Network Questionnaire are correlated with the SCAN method to demonstrate that the SCAN approach can find meaningful subgroups. Additional empirical findings are reported. Betweenness centrality appears to be a useful filter for screening potential subgroup members, and members of cohesive subgroups have stronger community membership and influence than others. Subgroups identified using weighted average hierarchical clustering are consistent with the subgroups identified using the more computationally expensive k-plex analysis. The value of similarity measurement in assessing subgroup cohesion over time is demonstrated, and possible problems with the use of Q modularity to identify cohesive subgroups are noted. Applications of this research to marketing, expertise location, and information search are also discussed.

Finding subgroups within social networks is important for understanding and possibly influencing the formation and evolution of online communities. This thesis addresses the problem of finding cohesive subgroups within social networks inferred from online interactions. The dissertation begins with a review of relevant literature and identifies existing methods for finding cohesive subgroups. This is followed by the introduction of the SCAN method for identifying subgroups in online interaction. The SCAN (Social Cohesion Analysis of Networks) methodology involves three steps: selecting the possible members (Select), collecting those members into possible subgroups (Collect) and choosing the cohesive subgroups over time (Choose). Social network analysis, clustering and partitioning, and similarity measurement are then used to implement each of the steps. Two further case studies are presented, one involving the TorCamp Google group and the other involving YouTube vaccination videos, to demonstrate how the methodology works in practice. Behavioural measures of Sense of Community and the Social Network Questionnaire are correlated with the SCAN method to demonstrate that the SCAN approach can find meaningful subgroups. Additional empirical findings are reported. Betweenness centrality appears to be a useful filter for screening potential subgroup members, and members of cohesive subgroups have stronger community membership and influence than others. Subgroups identified using weighted average hierarchical clustering are consistent with the subgroups identified using the more computationally expensive k-plex analysis. The value of similarity measurement in assessing subgroup cohesion over time is demonstrated, and possible problems with the use of Q modularity to identify cohesive subgroups are noted. Applications of this research to marketing, expertise location, and information search are also discussed.

Most of virtual communities are built on the client/server system. There are some limitations on the client/server system such as the maintenance cost and the personal attribute protection. The peer-to-peer system has some strengths to overcome the limitations of client/server system. Therefore, we are willing to export the virtual community on the peer-to-peer system. There are two main team formation approaches in the current virtual community collaboration. Either one of these approaches alone has its limitations. In this study, we adopt the social network concept to design a team formation mechanism in order to overcome the limitations of current approaches. Besides, because of the natural of peer-to-peer system, the exchange of messages is sending and receiving on the network. The mechanism proposed in this research can also reduce the traffic cost of the team formation process. Furthermore, it maintains the fitness of members who are chosen in the same team.

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.

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.