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Social Network Analysis

Social Network Analysis
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. The nodes in the network are the people and groups while the links show relationships or flows between the nodes. SNA provides both a visual and a mathematical analysis of human relationships. Management consultants use this methodology with their business clients and call it Organizational Network Analysis [ONA]. ONA allows you to x-ray your organization and reveal the managerial nervous system that connects everything. To understand networks and their participants, we evaluate the location and grouping of actors in the network. We look at a social network -- the "Kite Network" above -- developed by David Krackhardt, a leading researcher in social networks. Degree Centrality Betweenness Centrality Closeness Centrality Network Centralization Network Reach Not all network paths are created equal.

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Related:  data analysisArticles & PostsMedia, social media and New mediaSNA-4025

Qualrus - The Intelligent Qualitative Analysis Program Qualrus is an innovative qualitative data analysis tool that helps you manage unstructured data. Use it to analyze interviews, organize field notes, measure survey responses & more... or download the free demo Fast, accurate codingQualrus learns your coding trends and offers relevant suggestions as you go.

Your social networks and the secret story of metadata Researchers at MIT’s Media Lab have created an application called Immersion, which uses your email to display all of the people you communicate with in a highly visual way. Although it was designed primarily as a way of illustrating a person’s connections and social networks, it has served to highlight the amount of information that is encoded in communication metadata and what can be done with that data without even needing the actual content of emails. This is especially relevant at the moment with the revelations that the US secret service has been engaged in widespread surveillance using email and other personal data sourced from companies such as Google, Apple, Facebook and Microsoft. Email metadata refers to information such as the sender and recipients of an email.

Social Media Toolkit This is a collection of tips, recommendations, tools and pieces of social media best practice. Compiled by The Open University's Social Media Team, it’s primarily aimed at colleagues who use social media in a professional capacity. This includes: People managing accounts as The Open UniversityPeople managing accounts as a nation, faculty, department, unit or any other part of the OUAcademic staff with personal accounts who post about their work

Introduction to Social Network Methods:  Chapter 1: Social Network Data 1. Social network data On one hand, there really isn't anything about social network data that is all that unusual. Social network analysts do use a specialized language for describing the structure and contents of the sets of observations that they use. Google Obtains IBM Technology for Assessing Social Users' Interests Among a handful of patents transferred last December 31 from IBM's portfolio to that of Google, as first discovered by Bill Slawski of SEO By the Sea, is a system for processing text compiled by users of social networks, and ascertaining their common interests. We've already seen the rise of tools such as Radian6 for ascertaining social net users' individual interests; this new technology, which received a U.S. patent only one year ago, would judge what concepts they share with one another. The goal of this technology, as IBM originally stated, is to literally to filter out irrelevant links to articles that may not pertain to users' search intentions. What we don't know yet is whether Google intends to use this technology, or simply keep others from using it first.

Intelligent Archive / Centre for Literary and Linguistic Computing / Research Institutes, Centres & Groups / Research / Humanities and Social Science / Schools Developed at the Centre for Literary and Linguistic Computing , University of Newcastle, Australia Hugh Craig R Whipp, Michael Ralston Predictive Policing: Preventing Crime with Data and Analytics Wednesday, September 18th, 2013 - 14:52 In this report, Dr. Bachner tells compelling stories of how new policing approaches in communities are turning traditional police officers into “data detectives.” Police departments across the country have adapted business techniques -- initially developed by retailers, such as Netflix and WalMart, to predict consumer behavior -- to predict criminal behavior. The report presents case studies of the experiences of Santa Cruz, CA; Baltimore County, MD; and Richmond, VA, in using predictive policing as a new and effective tool to combat crime.

About - Digital Media Research Centre Our vision The Digital Media Research Centre (DMRC) conducts world-leading research that helps society understand and adapt to the social, cultural and economic transformations associated with digital media technologies. Aims and objectives Digital media have become a near-ubiquitous part of our everyday lives.

Introduction to Social Network Methods: Table of Contents Robert A. Hanneman and Mark Riddle Introduction to social network methods Table of contents In a networked world, why is the geography of knowledge still uneven? Children in an internet shop in Jakarta. The distribution of online knowledge is heavily weighted towards the global north. Photograph: Romeo Gacad/AFP/Getty Images Digital information – photographs, blogs, videos, tweets, Wikipedia articles, reviews, descriptions, stories, and myriad other types of content – surrounds us. Introduction and guide A short video guide to this site and to the Caqdas Networking Project site. This site is designed for several different kinds of user who have questions about QDA (qualitative data analysis) and CAQDAS (Computer Assisted Qualitative Data AnalysiS) programs. The links below are for some of these categories of users.

How to find communities online using social network analysis In my last two posts I introduced the Econsultancy Twitter network, and wrote about how we could use social network analysis to identify influencers and innovators in this community. In this post I'll look at how mapping a network can help us identify sub-groups in the community and target content to them more effectively. Detecting sub-groups in the Econsultancy Twitter network The most famous example of a sharply divided online community is Lada Adamic's analysis of US political bloggers during the 2004 presidential election.

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