@conference {17643, title = {Predicting Trust and Distrust in Social Networks}, booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)}, year = {2011}, month = {2011/10/09/11}, pages = {418 - 424}, publisher = {IEEE}, organization = {IEEE}, abstract = {As user-generated content and interactions have overtaken the web as the default mode of use, questions of whom and what to trust have become increasingly important. Fortunately, online social networks and social media have made it easy for users to indicate whom they trust and whom they do not. However, this does not solve the problem since each user is only likely to know a tiny fraction of other users, we must have methods for inferring trust - and distrust - between users who do not know one another. In this paper, we present a new method for computing both trust and distrust (i.e., positive and negative trust). We do this by combining an inference algorithm that relies on a probabilistic interpretation of trust based on random graphs with a modified spring-embedding algorithm. Our algorithm correctly classifies hidden trust edges as positive or negative with high accuracy. These results are useful in a wide range of social web applications where trust is important to user behavior and satisfaction.}, keywords = {distrust prediction, Electronic publishing, Encyclopedias, graph theory, inference algorithm, Inference algorithms, inference mechanisms, Internet, negative trust, online social networks, positive trust, Prediction algorithms, probability, random graphs, security of data, social media, social networking (online), spring-embedding algorithm, Training, trust inference, trust probabilistic interpretation, user behavior, user satisfaction, user-generated content, user-generated interactions}, isbn = {978-1-4577-1931-8}, doi = {10.1109/PASSAT/SocialCom.2011.56}, author = {DuBois,T. and Golbeck,J. and Srinivasan, Aravind} } @book {16975, title = {Analyzing Social Media Networks with NodeXL}, year = {2010}, month = {2010/08/27/}, publisher = {Morgan Kaufmann}, organization = {Morgan Kaufmann}, abstract = {"Analyzing Social Media Networks with NodeXL provides a much needed resource for the social media research community, as it describes network theory, provides compelling examples using data sources like Twitter and Flickr, and highlights how to use a free sophisticated tool for analysis. This is the perfect book for anyone trying to analyze the behavior of online social networks and beyond." ---Adam Perer, Research Scientist, IBM Research "This book provides a basic introduction to social network analysis, followed by practical instruction and examples on gathering data from online sources, importing into Excel, and then analyzing the data through Excel. The book will be important for promoting research in the area for those in information science, sociology, cultural studies, virtual community, and e-commerce."---Caroline Haythornthwaite, PhD, Professor, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign Businesses, entrepreneurs, individuals, and government agencies alike are looking to social network analysis (SNA) tools for insight into trends, connections, and fluctuations in social media. Microsoft{\textquoteright}s NodeXL is a free, open-source SNA plug-in for use with Excel. It provides instant graphical representation of relationships of complex networked data. But it goes further than other SNA tools - NodeXL was developed by a multidisciplinary team of experts that bring together information studies, computer science, sociology, human-computer interaction, and over 20 years of visual analytic theory and information visualization into a simple tool anyone can use. This makes NodeXL of interest not only to end-users but also to researchers and students studying visual and network analytics and their application in the real world.}, keywords = {Business \& Economics / Marketing / Research, Computers / Computer Science, Computers / Data Processing, Computers / Database Management / Data Mining, Computers / General, Computers / Information Theory, Computers / Interactive \& Multimedia, Computers / Social Aspects / General, Computers / Social Aspects / Human-Computer Interaction, Computers / User Interfaces, data mining, Data mining - Computer programs, Data mining/ Computer programs, Information Visualization, Information visualization - Computer programs, Information visualization/ Computer programs, NodeXL, online social networks, Social Science / General, Social Science / Reference, Social Science / Sociology / General, Technology \& Engineering / General, Technology \& Engineering / Social Aspects, Webometrics, Webometrics - Computer programs, Webometrics/ Computer programs}, isbn = {9780123822291}, author = {Hansen,Derek and Shneiderman, Ben and Smith,Marc A.} }