INTEGRATING CHANGE DETECTION AND SOCIAL NETWORK ANALYSIS FOR DECISION SUPPORT SYSTEM USING ASSOCIATIVITY METRICS IN NODEXL

Abstract

Social network analysis has become a fast growing body of research that has gained momentum since the beginning of the 2000s.   The current research paper provides an integration scheme with the help of decision-support systems that combines the methods of change detection with social network analytic techniques.The email messages of Mrs. Hillary Rodham Clinton, the former U.S. Secretary of State, are the subject of analysis based on both personal and professional email messages.   In 2015, Clinton was put under budgetary examination regarding using personal email addresses to conduct non-governmental work when she was in office.Other critics and critics of the government claim that this use is contrary to federal procedures, which are supposed to provide quality record keeping of government action; this led to a progression of Freedom-of-Information legal cases involving emails exchanged over her personal server. On Monday, August 31, the State Department published what to date remains the highest volume of Clinton emails totaling almost 7,000 pages; the files were accessible as PDF.   In our empirical study we use the NodeXL tool to analyze the email messages of 2011-12 and calculate the indices of closeness centrality, betweenness centrality, eigenvector centrality, and PageRank.Though these issues have been addressed by previous researchers, our research has added to the issue by conducting a more accurate analysis of these centrality measures by further considering the concept of associativity centrality in the nodexl framework. It is based on the analysis of the data that is represented as a clean and standardized CSV representation, obtained in an online repository.