A Twitter Social Network analysis based on graph and network theory : the South African Health Insurance Bill Case

Abstract:Social Network Analysis (SNA) is the process of extricating relationships and interchanges among firms, individuals and connected information objects by way of visual mapping. Through the lenses of graph theory and network theory, this study aims to explore the Twitter social media network shortly after the introduction of the National Health Insurance (NHI) Bill to the South African parliament was announced for debate. In graph theory, algorithms are used to extract knowledge and efficient visualisation techniques to represent, for the purpose of this study, pairwise relations between objects, namely Twitter data. An instrumental, single case study design and SNA (based on network theory principles) secured contextual and timely Twitter interchanges of 4 112 tweets of the hashtag “NHI”. The uniqueness of this inquiry is the use of the ‘Network Overview, Discovery and Exploration for Excel Pro’ (NodeXL Pro) tool for social media analytics to simplify the Twitter SNA tasks and analysis of the #NHI twitter social media network. The findings explain the data dispersion and network structure of the #NHI case. The significance of the study is that the SNA clearly identifies the influencers, popular Twitter users and gatekeepers in the announcement of a highly controversial healthcare bill that will affect all South African citizens

Society seen through the prism of space: outline of a theory of society and space

Two questions challenge the student of space and society above all others: will new technologies change the spatial basis of society ? And if so, will this have an impact on society itself ? For the urbanist, these two questions crystallise into one: what will the future of cities have to do with their past ? Too often these questions are dealt with as though they were only matters of technology. But they are much more than that. They are deep and difficult questions about the interdependence of technology, space and society that we do not yet have the theoretical apparatus to answer. We know that previous �revolutions� in technology such as agriculture, urbanism and industrialisation associated radical changes in space with no less radical changes in social institutions. But we do not know how far these linkages were contingent or necessary. We do not, in short, have a theory of society and space adequate to account for where we are now, and therefore we have no reasonable theoretical base for speculating about the future. In this paper, I suggest that a major reason for this theoretical deficit is that most previous attempts to build a theory of society and space have looked at society and tried to find space in its output. The result has been that the constructive role of space in creating and and sustaining society has not been brought to the fore, or if it has, only in a way which is too general to permit the detailed specification of mechanisms. In this paper I try to reverse the normal order of things this by looking first at space and trying the discern society through space: by looking at society through the prism of space. Through this I try to define key mechanisms linking space to society and then use these to suggest how the questions about the future of cities and societies might be better defined

Modeling social networks from sampled data

Network models are widely used to represent relational information among interacting units and the structural implications of these relations. Recently, social network studies have focused a great deal of attention on random graph models of networks whose nodes represent individual social actors and whose edges represent a specified relationship between the actors. Most inference for social network models assumes that the presence or absence of all possible links is observed, that the information is completely reliable, and that there are no measurement (e.g., recording) errors. This is clearly not true in practice, as much network data is collected though sample surveys. In addition even if a census of a population is attempted, individuals and links between individuals are missed (i.e., do not appear in the recorded data). In this paper we develop the conceptual and computational theory for inference based on sampled network information. We first review forms of network sampling designs used in practice. We consider inference from the likelihood framework, and develop a typology of network data that reflects their treatment within this frame. We then develop inference for social network models based on information from adaptive network designs. We motivate and illustrate these ideas by analyzing the effect of link-tracing sampling designs on a collaboration network.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS221 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A survey of statistical network models

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference

An investigation of the relation of space to society: a discussion on A. Giddens, H. Lefebvre and space syntax

This thesis is dealing with the relation of society and space as a main characteristic for elucidating the design process. More particular is based on the problem which appears both in spatial and social theories of relating entities which ‘are in different scales’. This is the relation of space, which is a local notion, to society, which is a global idea or the relation of society to the everyday life, which is also local and spatial. Thιs thesis attempts to investigate the relation of society to space through this core problem by examining three theories which seem to deal with this issue. These are the Space Syntax Theory of Hillier and Hanson, the Structuration theory of Giddens and the theory of the Production of Space of Lefebvre. The first has an architectural and urban point of view of the matter, the second a sociological and the third a politico-economic. The discussion of the three theories shows that all three grasp an interrelation between society and space although each theory sees this interrelation in a different way. For the Structuration theory space has an important role in the structuration of society, for Space Syntax a constructive role of the generic forms of society and for Lefebvre an instrumental character