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 It should also be useful to staff who are interested in starting social media accounts or learning new ways to use existing ones. You can still read our guidance even if you're not part of The Open University. This isn’t a definitive guide. If you’ve got any questions about the site, suggestions of new things to add, or you want to share a great tip or success story, don’t hesitate to get in touch. Get started with Twitter Interested in Twitter but not sure where to begin? Advice from OU academics Lots of OU academics are active on social media.
Choosing people: the role of social capital in information seeking behaviour Catherine A. Johnson School of Information Studies University of Wisconsin-Milwaukee Milwaukee, Wisconsin 53202, USA Abstract It is an almost universal finding in studies investigating human information behaviour that people choose other people as their preferred source of information. An explanation for the use of people as information sources is that they are easier to approach than more formal sources and therefore are a least effort option. However there have been few studies that have investigated who the people chosen as information sources are and what their relationship to the information seeker is. Introduction It is an almost universal finding in studies investigating human information behaviour that people choose other people as their preferred source of information. The explanation for the use of people as information sources has often been that they are 'typically easier and more readily accessible than the most authoritative printed sources' (Case, 2002: 142). Methodology
What does it mean to be posthuman? By David Cohen HOW would you like to be a posthuman? You know, a person who has gone beyond the “maximum attainable capacities by any current human being without recourse to new technological means”, as philosopher Nick Bostrum of the Future of Humanity Institute at the University of Oxford so carefully described it in a recent paper. In other words, a superbeing by today’s standards. If this sounds like hyperbole, bear with me. Advertisement No, we mean people who, through genetic manipulation, the use of stem cells, or other biointervention, have had their ability to remain healthy and active extended beyond what we would consider normal. “Whatever it means to be ‘posthuman’, this discussion is too important to be left to academics” Is it possible to imagine such humans without recourse to science fiction clichés? If this seems a stretch, consider this: preimplantation genetic diagnosis already lets us screen out some genetic abnormalities in our IVF offspring. More on these topics:
What is Social Network Analysis? What is Network Analysis? Network analysis is the study of social relations among a set of actors. It is a field of study -- a set of phenomena or data which we seek to understand. In the process of working in this field, network researchers have developed a set of distinctive theoretical perspectives as well. focus on relationships between actors rather than attributes of actors sense of interdependence: a molecular rather atomistic view structure affects substantive outcomes emergent effects Network theory is sympathetic with systems theory and complexity theory. Social networks is also characterized by a distinctive methodology encompassing techniques for collecting data, statistical analysis, visual representation, etc. Social Relations Social relations can be thought of as dyadic attributes. Topics 1. Attributes of ego network --> access to resources, mental/physical health Network closeness --> influence, diffusion Similarity of position --> similarity of risks, opportunities, outcomes
Introduction to Social Network Methods: Table of Contents Robert A. Hanneman and Mark Riddle Introduction to social network methods Table of contents About this book This on-line textbook introduces many of the basics of formal approaches to the analysis of social networks. You are invited to use and redistribute this text freely -- but please acknowledge the source. Hanneman, Robert A. and Mark Riddle. 2005. Table of contents: Preface1. 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. New technological developments like big data, locative media and wearable technologies challenge social science and humanities researchers to develop new approaches and methods, and to train upcoming researchers in how to apply them. The centre draws on QUT's research strengths in media, communication, cultural and journalism studies, as well as law, economics and education across a number of problem-focused research programs. We aim to: Who are we? The DMRC is based in the Creative Industries Faculty, with collaborators in the law, science and engineering, education, and business faculties. The centre is directed by Professor Jean Burgess.
Communities of practice The term “community of practice” is of relatively recent coinage, even though the phenomenon it refers to is age-old. The concept has turned out to provide a useful perspective on knowing and learning. A growing number of people and organizations in various sectors are now focusing on communities of practice as a key to improving their performance.This brief and general introduction examines what communities of practice are and why researchers and practitioners in so many different contexts find them useful as an approach to knowing and learning. What are communities of practice? Note that this definition allows for, but does not assume, intentionality: learning can be the reason the community comes together or an incidental outcome of member’s interactions. Not everything called a community is a community of practice. The domain: A community of practice is not merely a club of friends or a network of connections between people. What do communities of practice look like? Organizations.
Debates in the Digital Humanities 2011, tools, quarterly, victoria, now, jobs, projects, startup grant, companion, blog —Top ten Google Instant appendages to a search on “digital humanities” as of April 28, 2011, 10:35 AM EDT This Strange Confluence Digital humanities is a tactical term. In a previous essay, “What Is Digital Humanities and What’s It Doing in English Departments?” I suggested that for those seeking to define digital humanities, the then-current Wikipedia definition (and top Google hit) served about as well as any and could save a lot of headache and, second, that the term “digital humanities” itself has a specific, recoverable history, originating with circumstances (which I documented) having primarily to do with marketing and uptake, and, third, that the term is now being “wielded instrumentally” by those seeking to effect change “amid the increasingly monstrous institutional terrain” of the contemporary academy. The institutional structures we create thus tend to have long half-lives. And the Name
Social network analysis: Wikipedia Social network analysis (SNA) is the analysis of social networks. Social network analysis views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as friendship, kinship, organizations, sexual relationships, etc.)[1][2] These networks are often depicted in a social network diagram, where nodes are represented as points and ties are represented as lines. Overview[edit] Social network analysis has emerged as a key technique in modern sociology. Metrics[edit] Connections[edit] Homophily: The extent to which actors form ties with similar versus dissimilar others. Multiplexity: The number of content-forms contained in a tie.[12] For example, two people who are friends and also work together would have a multiplexity of 2.[13] Multiplexity has been associated with relationship strength. Distributions[edit] Segmentation[edit] Practical applications[edit]
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. On the other hand, the data sets that social network analysts develop usually end up looking quite different from the conventional rectangular data array so familiar to survey researchers and statistical analysts. "Conventional" social science data consist of a rectangular array of measurements. Figure 1.1. The fundamental data structure is one that leads us to compare how actors are similar or dissimilar to each other across attributes (by comparing rows). "Network" data (in their purest form) consist of a square array of measurements. Figure 1.2. We could look at this data structure the same way as with attribute data. But a network analyst is also likely to look at the data structure in a second way -- holistically. table of contents
The Art of Molly Crabapple The Turn: Integration of Information Seeking and Retrieval in Context The Turn: Integration of Information Seeking and Retrieval in Context. Peter Ingwersen & Kalervo Järvelin. Dordrecht, The Netherlands: Springer, 2005, XIV, 448 p., Hardcover, ISBN 1-4020-3850-X, € 59,95 In July 2004, I was one of the participants at the Workshop on Information Retrieval in Context (IRiX) which was held in conjunction with SIGIR conference. The Turn is a valuable book which aims to demonstrate how and why IR and IS research should move side by side. The book consists of nine well-organized chapters including introduction, the cognitive framework for information, the development of information seeking research, system-oriented information retrieval, cognitive and user-oriented information retrieval, the integral IS&R research framework, implications of cognitive framework for IS&R, towards a research program, and conclusion. One of the main features of the book which somehow makes it distinctive from other IR and IS books is its cognitive viewpoint in IR&S. Footnotes: