Web and mobile technologies have had such profound impact that we have witnessed significant evolutionary changes in our social, economic and cultural activities. In recent years, online social networking sites such as Twitter, Facebook, Google+, and LinkedIn have gained immense popularity. Such social networks have led to an enormous explosion of network-centric data in a wide variety scenarios, posing unprecedented analytical and computational challenges to MIS researchers. At the same time, the availability of such data offers major research opportunities in various social computing and analytics areas to tackle interesting questions such as: - From a business and marketing perspective, how to mine the novel datasets of online user activities, interpersonal communications and interactions, for developing more successful marketing strategies? - From a system development perspective, how to incorporate massive amounts of available data to assist online users to find relevant, efficient, and timely information? In this dissertation, I explored these research opportunities by studying multiple analytics problems arose from the design and use of social networking services. The first two chapters (Chapter 2 and 3) are intended to study how social network can help to derive a better estimation of customer lifetime value (CLV), in the social gaming context. In Chapter 2, I first conducted an empirical study to demonstrate that friends' activities can serve as significant indicators of a player's CLV. Based on this observation, I proposed a perceptron-based online CLV prediction model considering both individual and friendship information. Preliminary results have shown that the model can be effectively used in online CLV prediction, by evaluating against other commonly-used benchmark methods. In Chapter 3, I further extended the metric of traditional CLV, by incorporating the personal influences on other customers' purchase as an integral part of the lifetime value. The proposed metric was illustrated and tested on seven social games of different genres. The results showed that the new metric can help marketing managers to achieve more successful marketing decisions in user acquisition, user retention, and cross promotion. Chapter 4 is devoted to the design of a recommendation system for micro-blogging. I studied the information diffusion pattern in a micro-blogging site (Twitter.com) and proposed diffusion-based metrics to assess the quality of micro-blogs, and leverage the new metric to implement a novel recommendation framework to help micro-blogging users to efficiently identify quality news feeds. Chapter 5 concludes this dissertation by highlighting major research contributions and future directions.

[pt] A propagação de influências tem sido objeto de extensos estudos devido a seu importante impacto em redes sociais, epidemiologia e muitas outras áreas. A compreensão do mecanismo de propagação é crítica, por exemplo, para controlar a disseminação de notícias falsas ou controlar uma epidemia. Neste trabalho, seguimos uma perspectiva de otimização e identificamos o menor grupo de usuários que precisam ser convertidos para atingir um certo nível de influência em toda a rede. Portanto, estudamos formalmente o problema de influência de menor custo generalizado, propondo algoritmos de programação matemática para resolver este problema. Introduzimos novos algoritmos de planos de corte e separação, e os incorporamos em um algoritmo de branch-and-cut. Nossos resultados experimentais em instâncias da literatura demonstram a capacidade do método de resolver pequenas e médias instâncias, bem como diminuir o gap da melhor solução conhecida e inclusive encontrando também soluções ótimas para alguns problemas em aberto. / [en] Influence propagation has been the subject of extensive study due to its important role in social networks, epidemiology, and many other areas. Understanding the propagation mechanism is critical, e.g., to control the spread of fake news or to control an epidemic. In this work, we follow an optimization perspective, and attempt to identify the smallest group of users that needs to be converted to achieve an certain influence level over the entire network. We therefore formally study the generalized least cost influence problem, proposing mathematical programming algorithms to solve the challenging problem. We introduce new cutting plane and separation algorithms and embed them into a branch-and-cut algorithm. Our experimental results on classical benchmark instances demonstrates the method ability to solve small-to medium-scale benchmark instances, also finding optimal solutions for some open problems.