Conforme o volume e a diversidade de informações científicas aumentam, se torna necessário entender o que, porque e como esse aumento acontece. Estratégias e políticas públicas podem se desenvolver a partir dessas informações potencializando os serviços de educação e inovação oferecidos à sociedade. A análise de tendências é um dos passos nessa direção. Este trabalho no entanto vai além de considerar apenas o conteúdo das informações analisadas incluindo a estrutura das fontes geradoras das informações, ou seja, as redes sociais, como uma dimensão adicional para modelar e predizer tendências ao longo do tempo. Os experimentos foram realizados com os títulos das publicações de todos os doutores brasileiros da área de Ciência da Computação. Os resultados mostraram que a incorporação das medidas oriundas da análise de redes sociais reduziram os erros de predição, na média, para cerca de 18% daqueles produzidos sem a utilização destas medidas. Adicionalmente, esta incorporação permitiu que previsões mais futuras fossem realizadas sem grandes aumentos no erro destas previsões / As scientific information volume and diversity grow, the understanding of what, why and how it happens become necessary. Strategies and public politcs can be developed from these informations to power innovation and education services offered to society. Trend analysis is one of the steps in this direction. This work however goes beyond of just anlyse information content, it includes the information about the information sources structures, in other words, the social networks, as another dimension to model and predict trends through time. The experiments were made with the publication titles of all Brazilian Computer Science`s PHds. The results indicate the use o social network analysis metrics reduced the precision errors to about 18% of the errors produced without considering these metrics

Conforme o volume e a diversidade de informações científicas aumentam, se torna necessário entender o que, porque e como esse aumento acontece. Estratégias e políticas públicas podem se desenvolver a partir dessas informações potencializando os serviços de educação e inovação oferecidos à sociedade. A análise de tendências é um dos passos nessa direção. Este trabalho no entanto vai além de considerar apenas o conteúdo das informações analisadas incluindo a estrutura das fontes geradoras das informações, ou seja, as redes sociais, como uma dimensão adicional para modelar e predizer tendências ao longo do tempo. Os experimentos foram realizados com os títulos das publicações de todos os doutores brasileiros da área de Ciência da Computação. Os resultados mostraram que a incorporação das medidas oriundas da análise de redes sociais reduziram os erros de predição, na média, para cerca de 18% daqueles produzidos sem a utilização destas medidas. Adicionalmente, esta incorporação permitiu que previsões mais futuras fossem realizadas sem grandes aumentos no erro destas previsões / As scientific information volume and diversity grow, the understanding of what, why and how it happens become necessary. Strategies and public politcs can be developed from these informations to power innovation and education services offered to society. Trend analysis is one of the steps in this direction. This work however goes beyond of just anlyse information content, it includes the information about the information sources structures, in other words, the social networks, as another dimension to model and predict trends through time. The experiments were made with the publication titles of all Brazilian Computer Science`s PHds. The results indicate the use o social network analysis metrics reduced the precision errors to about 18% of the errors produced without considering these metrics

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Social networking sites are among the most popular daily activities of students these days. Students are mostly using social networking sites for communication and sharing of their experiences. Facebook is an example of a social networking site, which supports additional features such as creating a profile page, creating group pages and supports possibility of implementing different integrated application with Facebook. These features improve the Facebook experience, allowing users to form groups, where they can introduce ideas and concepts, which can be shared and discussed in a structured style. For this thesis we have created a new learning management system by implementing an online educational platform within a Facebook context. This work introduces a new, complementary style of education, where students can improve their knowledge and sociality outside the university in an innovative way. The platform takes advantage of gamification, which introduces game-like elements to concepts such as education and learning management systems, to make them more fun and rewarding. The goal of this thesis is to extend the educational border to an interesting online environment where students can learn, communicate, and examine their knowledge globally in different courses within our application platform in Facebook.

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.

In recent years, many virtual communities have thrived with the rapid development of Internet. In the environment of virtual community, through the information exchange, members realize that they often have similar demands. In the case of online group-buying, buyers who have a common interest in a certain product group together to collect their collective power and thus get price discounts from suppliers. Hence online group-buying comprehends the need of interest, relationship, and transaction of virtual community. We are questioning whether the group-buying have invisible community relationships embedded in members¡¦ transaction activities. The purpose of this study is by the use of social network analysis to investigate the relationships of the network between initiators and participants and between co-participants. In particular we propose some measures based on social network analysis to help us understand the meanings of relationships of online group-buying network. According to the result of this study, we find the initiators have more power or resources to influence other members of the network between initiators and participants. And some active participants also have more power or resources to influence other participants of the network between co-participants. We also find the active initiators and participants would have more probability to occupy key positions of information flow.

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.

Interpersonal relationship and recommendation are the important relation and popular mechanism. Living in the information-overloading age, the original information searching mechanisms, which require the specification of keywords, are ineffective and impractical. Moreover, a variety of recommendation techniques have been proposed and many of them have been implemented in real systems, especially in online stores. Among different recommendation techniques proposed in the literature, the content-based and collaborative filtering approaches have been broadly adopted by membership stores that maintain users¡¦ long term interest. For short-term interest, by far the content-based approach is the most popular one for recommendation. However, most of the proposed recommendation approaches do not take the social information as an important factor. In this study, we proposed several social network-based recommendation approaches that take into account the similarities of items with respect to their social closeness for meeting users¡¦ short term interests. Our experiment evaluation results show that social network-based approaches perform better than the content-based counterpart, if the user¡¦s short term interest profile contains articles of similar content. Nonetheless, content-based approach becomes better when articles in the profile are diversified in their content. Besides, contrast to content-based approach, social network-based approach is less likely to recommend articles which readers do not value. Finally, the desired articles recommended by content-based approach are very different from those by social network-based approach. This suggests the development of some hybrid recommendation method that utilizes both content and social network when making recommendations.

Living in an information-overloading age, the original information searching mechanisms are ineffective and impractical. As the e-commerce is more and more popular, using information technology to discover the latent demand of customers becomes an important issue. Hence, a variety of recommendation techniques have been proposed and many of them have been implemented in real systems, mostly in online stores. Among the techniques, the content-based and collaborative filtering approaches are the ones broadly adopted and proved to be successful. Recently, social network-based recommendation approach has been proposed that takes into account the similarities of items with respect to their social closeness. The social network-based approach performs better than content-based approach in some scenarios and it can also avoid recommending articles that have high content similarity to a user¡¦s favorite articles but low quality. Therefore, we propose three hybrid approaches, Switching, Proportional, and Fusion that combine content-based and social network-based approaches in order to achieve a better performance. Our experimental result shows that even though the proposed approaches have pros and cons under different scenarios, in general they achieve better performance than individual approaches. Besides, we generate some synthetic articles that have close content similarities to articles in our collection to evaluate the fidelity of each approach. The experimental results show that approaches incorporating social network information have lower chance to recommend these faked articles.