Abstract. This paper investigates in the effects of using electronic communication forms in webbased environments. Following the idea of triangulation, we used qualitative methods, statistical analysis and Social Network Analyses to explore the patterns of communication within one selected case of a mixed presence/web-based university course. The results show that while an isolated perspective does not suffice to explain the complex processes, taking more perspectives into account in a combined and integrated way provides a better understanding of technology enabled communication and interaction.

Although there is a large body of work concerned with information moving from person to person via "word of mouth" mechanisms, mathematical modeling of message content per se continues to be underdeveloped. Such models are of particular concern in the area of crisis response, wherein the need for accurate situation assessment based on informant reports motivates detailed modeling of information transmission among persons at an incident site. As a first step towards the modeling of information transmission in crisis contexts, we introduce a simple model based on prior findings from the literature on rumor propagation and informant accuracy. This model is calibrated using data from Allport and Postman's (1947) famous information transmission study, and various implications of the model for the fidelity of information transmission are explored.

Abstract—Typical analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long history of their successful use. However, modeling of covert, terrorist or criminal networks through social graphs do not really provide the hierarchical structure of such networks because these networks are composed of leaders and followers. In this short paper we investigate small world networks by computing first the Bayes posteriori probability which is then used to calculate the entropy of the network. The computed probability and entropy distribution further utilized in predicting the command structure of the network.

Abstract. Typical analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long history of their successful use. However, modeling of covert, terrorist or criminal networks through social graph dose not really provide the hierarchical structure of such networks because these networks are composed of leaders and followers. It is possible mathematically, for some graphs to estimate the probability that the removal of a certain number of nodes would split the networks into may be non functional network. In this research we investigate and analyze a social network using Bayes probability theory model to calculate entropy of each node present in the network to high light the important actors in the network. This is accomplished by observing the amount of entropy change computed by successively removing each node in the network.