Understanding Silent Users’ Behavior in Large-scale Online Communities
Andrea Tagarelli and Roberto Interdonato – University of Calabria, Italy
Research on social networks (SNs) has traditionally focused on influential users, experts and trendsetters. By contrast, poor attention has been paid to the fact that all large-scale on-line communities are characterized by a participation inequality principle, i.e., the crowd of a SN does not actively contribute, rather it lurks. Lurkers are those silent members of a SN who gain benefit from others' information without giving back to the SN. However, because they acquire knowledge from the SN, lurkers are social capital holders. Within this view, a major goal is to de-lurk such users, that is, to encourage them to more actively participate in the SN. Developing solutions to de-lurking problems can support enhanced personalization of user access and adaptation of the design of web-based systems and their interfaces, thus ultimately helping sustain a community over time with fresh ideas and perspectives.
Lurking analysis in SNs lies at the confluence of many disciplines, such as social science, human-computer interaction, and computer science. The goal of this tutorial is to provide an overview of research issues and solutions related to the characterization and analysis of lurkers in SNs. More specifically, it will explain the several meanings of “lurking” and related implications, introduce the principles and motivational factors underlying lurking behaviors, and discuss the main strategies of de-lurking. It will also guide through the computational approaches and methods to mine lurkers and unveil their behavioral patterns in social networks. It will further cover use-cases in different types of online social environments, and present perspectives and challenges in the field.
|Andrea Tagarelli is an assistant professor of computer engineering at the University of Calabria, Italy. From the same university, he received his Ph.D. in computer and systems engineering in 2006. He also obtained the Italian national scientific qualification to associate professor in 2013. His research interests are in the areas of data/text mining, network analysis and mining, web and semistructured data management, information retrieval.
He was co-organizer of three workshops and a mini-symposium on data mining topics in premier conferences in the field (KDD, SDM, PAKDD, ECML-PKDD). Since 2010, he has served on the program committee of the Advances in Social Network Analysis and Mining (ASONAM) conference. More information on his research, professional, and educational activities can be found at http://uweb.dimes.unical.it/tagarelli/.
|Roberto Interdonato is a third-year Ph.D. student at the University of Calabria, Italy. His Ph.D. work focuses on novel ranking problems in information networks. He is interested in network analysis, information retrieval and data mining topics, with emphasis on social networks, semantic networks, collaboration networks, and recommender systems.|
Empirical Evaluation of User Modeling Systems
David N. Chin – University of Hawaii
Date: Monday 29 June 2015 (Morning session)
This tutorial will introduce UM researchers to the techniques of empirical evaluation of user modeling systems. No background in statistics is required. The target audience is UM researchers, especially students, who have a background in computer science or some other field that does not normally include designing and running human-subject experiments. Topics include:
|I. Experiment Design|
|A. Independent vs. dependent variables
B. Nuisance variables
C. Between-subjects vs. within-subjects designs
D. Estimating sensitivity
E. Factorial designs
F. Layered evaluation
|II. Running Experiments|
B. Controlling the environment
C. Recording data
|III. Experiment Analysis|
|A. Means and variance
B. Statistical tests
D. Explained variance
|David N. Chin is a Professor and Chair of Information and Computer Sciences at the University of Hawaiʻi. He has served on the editorial board of User Modeling and User-Adapted Interaction since its inception in 1990, and guest edited a special issue of the journal on empirical evaluation (Volume 12 issues 2-3). He served on the organizing committees of the 2001, 2003, and 2005 User Modeling Conference Workshops on Empirical Evaluation of Adaptive Systems and has presented four previous versions of this tutorial at User Modeling/UMAP Conferences (1999, 2003, 2007, 2012). He has also written a review article on empirical evaluation.
Social Information Access
Peter Brusilovsky - University of Pittsburgh
The power of the modern Web, which is frequently called the Social Web or Web 2.0, is frequently traced to the power of users as contributors of various kinds of content through Wikis, blogs, question answers, reviews, and resource sharing sites. However, the community power impacts not only the production, but also the access to all kinds of Web content. A number of research groups worldwide explore what we call social information access techniques that help users get to the right information using “collective wisdom” distilled from actions of those who worked with this information earlier.
Social information access can be formally defined as a stream of research that explores methods for organizing users' past interaction with an information system (known as explicit and implicit feedback), in order to provide better access to information to the future users of the system. It covers a range of rather different systems and technologies from social navigation to collaborative filtering. An important feature of all social information access systems is self-organization. Social information access systems are able to work with little or no involvement of human indexers, organizers, or other kinds of experts. They are truly powered by a community of users. Due to this feature, social information access technologies are frequently considered as an alternative to the traditional (content-oriented) personalization technologies. The goal of this tutorial is to provide an overview of the emerging social information access research stream and to provide some practical guidelines for building social.
Peter Brusilovsky is a Professor of Information Science and Intelligent Systems at the University of Pittsburgh, where he directs Personalized Adaptive Web Systems (PAWS) lab. Peter has been working in the field of adaptive educational systems, user modeling, and intelligent user interfaces for over 20 years. He published numerous papers and edited several books on adaptive hypermedia and the adaptive Web. Peter is the Editor-in-Chief of IEEE Transactions on Learning Technologies and a board member of several journals including User Modeling and User Adapted Interaction, ACM Transactions on the Web, and Web Intelligence and Agent Systems. He is also the immediate past President of User Modeling Inc., a professional association
Peter Brusilovsky, Professor