Understanding Behavioral Drivers in Twitter Social Media Networks

As social media platforms facilitate user interactions, organizations increasingly use social media networks (SMNs) to build network ties. Studying user behavior on SMNs can help to uncover strategic information and improve situation awareness. However, there is a lack of understanding of behavioral drivers of SMN participants. This research developed a theoretically-based IS development framework for modeling user behavior in large evolving SMNs. To demonstrate the feasibility of our framework, we developed a proof-of-concept system for simulating user activities in the SMNs of Twitter social communities. Our system models the complex behavioral features in the SMNs by using a wide range of theoretically-driven features and machine-discovered features, and predicts user activities by using a pipeline of statistical and machine-learning techniques. Preliminary results of a simulation study provide insights of the importance of comprehensive network features to model SMN group behavior accurately and quality of commitment features to model SMN user behavior

Spatial interactions in agent-based modeling

Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means. The chapter reviews different approaches for modeling agents' behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution of economic activities, - out of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with spatial structure, is used to illustrate the potential of such an approach for spatial policy analysis.Comment: 26 pages, 5 figures, 105 references; a chapter prepared for the book "Complexity and Geographical Economics - Topics and Tools", P. Commendatore, S.S. Kayam and I. Kubin, Eds. (Springer, in press, 2014

Predictive Non-equilibrium Social Science

Non-Equilibrium Social Science (NESS) emphasizes dynamical phenomena, for instance the way political movements emerge or competing organizations interact. This paper argues that predictive analysis is an essential element of NESS, occupying a central role in its scientific inquiry and representing a key activity of practitioners in domains such as economics, public policy, and national security. We begin by clarifying the distinction between models which are useful for prediction and the much more common explanatory models studied in the social sciences. We then investigate a challenging real-world predictive analysis case study, and find evidence that the poor performance of standard prediction methods does not indicate an absence of human predictability but instead reflects (1.) incorrect assumptions concerning the predictive utility of explanatory models, (2.) misunderstanding regarding which features of social dynamics actually possess predictive power, and (3.) practical difficulties exploiting predictive representations.Comment: arXiv admin note: substantial text overlap with arXiv:1212.680

Graphical Models in Characterizing the Dependency Relationship in Wireless Networks and Social Networks

Semi-Markov processes have become increasingly important in probability and statistical modeling, which have found applications in traffic analysis, reliability and maintenance, survival analysis, performance evaluation, biology, DNA analysis, risk processes, insurance and finance, earthquake modeling, etc. In the first part of this thesis, our focus is on applying semi-Markov processes to modeling the on-off duty cycles of different nodes in wireless networks. More specifically, we are interested in restoration of statistics of individual occupancy patterns of specific users based on wireless RF observation traces. In particular, we present a novel approach to finding the statistics of several operations, namely down-sampling, superposition and mislabelling, of a discrete time semi-Markov process in terms of the sojourn time distributions and states transition matrix of the resulting process. The resulting process, after those operations, is also a semi-Markov processes or a Markov renewal process. We show that the statistics of the original sequence before the superposition operation of two semi Markov processes can be generally recovered. However the statistics of the original sequence cannot be recovered under the down-sampling operation, namely the probability transition matrix and the sojourn time distribution properties are distorted after the down-sampling. Simulation and numerical results further demonstrate the validity of our theoretical findings. Our results thus provide a more profound understanding on the limitation of applying semi-Markov models in characterizing and learning the dynamics of nodes\u27 activities in wireless networks. In the second portion of the thesis a review is provided about several graphical models that have been widely used in literature recently to characterize the relationships between different users in social networks, the influence of the neighboring nodes in the networks or the semantic similarity in different contexts