User behavioral problems in complex social networks

Abstract

Over the past two decades, we witnessed the advent and the rapid growth of numerous social networking platforms. Their pervasive diffusion dramatically changed the way we communicate and socialize with each other. They introduce new paradigms and impose new constraints within their scope. On the other hand, online social networks (OSNs) provide scientists an unprecedented opportunity to observe, in a controlled way, human behaviors. The goal of the research project described in this thesis is to design and develop tools in the context of network science and machine learning, to analyze, characterize and ultimately describe user behaviors in OSNs. After a brief review of network-science centrality measures and ranking algorithms, we examine the role of trust in OSNs, by proposing a new inference method for controversial situations. Afterward, we delve into social boundary spanning theory and define a ranking algorithm to rank and consequently identify users characterized by alternate behavior across OSNs. The second part of this thesis deals with machine-learning-based approaches to solve problems of learning a ranking function to identify lurkers and bots in OSNs. In the last part of this thesis, we discuss methods and techniques on how to learn a new representational space of entities in a multilayer social network.