The ICWSM-13 Committee is pleased to present the Tutorials Day program for the Seventh International Conference on Weblogs and Social Media (ICWSM-13) in Boston, USA. The Tutorials Day provides an opportunity for junior and senior researchers to spend a day, freely exploring exciting advances in disciplines outside their normal focus.
AM Thursday, July 11:
T3: Crisis Mapping, Citizen Sensing, and Social Media Analystics
T4: Multiple Network Models for Complex Online Social Network Analysis
T5: Pulse of Virtual Worlds
PM Thursday, July 11:
T1: Advanced Methods for Collecting Social Science Data in the Social Media Field
T2: Information-Theoretic Tools for Social Media Analysis
Presenter: Riki Conrey
Valid social and psychological data are crucial to answering the most interesting questions in social media. We need these data to parameterize agent-based models; append motivational information to social graph data; measure perceptions of risk in online decisions; and, in general, to put social media behavioral data in a human context. Collecting valid information about the unseen content of human cognition is difficult, but computational social scientists often focus their energies on the computational challenges associated with the research. Compared to calculating clustering in a massive graph, conducting an online survey seems easy. Posting an online poll is easy. Conducting an online survey that yields valid results is difficult and sometimes impossible. In this tutorial, we will discuss advanced methods for measuring human cognition, motivation, and decision-making. We will cover:
Presenters: Greg Ver Steeg and Aram Galstyan
Social media is a collection of moving targets. Both the platforms and the behaviors of the users of these platforms are diverse and constantly evolving. Ad hoc models based on assumptions about today’s users may not hold tomorrow. Information theory provides a general framework for identifying meaningful signals without relying on assumptions about human behavior or on platform-specific implementation details. The flexibility of the information-theoretic approach allows researchers to go beyond the study of "re-tweets" to consider rich data including textual content, timing, and context.
The main objective of this tutorial is to provide a gentle introduction to basic information-theoretic concepts and to demonstrate how those concepts can be applied in the context of social network analysis. In particular, we emphasize an interpretation of these quantities as measures of predictability. The strongest signals in social media, and the ones most amenable to research, are the ones that most predictably lead to change. We will use several case studies to illustrate how information theory can be fruitfully applied to real-world social media and to demonstrate how this analysis can be simplified with available tools.
Presenters: Amit Sheth, Patrick Meier, Carlos Castillo, and Hemant Purohit
With the explosion in social media (1B+ Facebook users, 500M+ Twitter users) and ubiquitous mobile access (6B+ mobile phone subscribers) sharing their observations and opinions, we have unprecedented opportunities to extract social signals, create spatio-temporal mappings, perform analytics on social data, and support applications that vary from situational awareness during crisis response, preparedness and rebuilding phases to advanced analytics on social data, and gaining valuable insights to support improved decision making.
This tutorial weaves three themes and corresponding relevant topics- a.) citizen sensing and crisis mapping, b.) technical challenges and recent research for leveraging citizen sensing to improve crisis response coordination, and c.) experiences in building robust and scalable platforms/systems. It will couple technical insights with identification of computational techniques and algorithms along with real-world examples. We will also do live demonstrations of the Ushahidi and Twitris platforms while elaborating on the practical issues and pitfalls of the development and operation of these large-scale platforms, especially during the real-time crisis response.Bio:
Presenters: Matteo Magnani and Luca Rossi
Multiple network models are fundamental to provide accurate analyses of human relationships. For example, while Facebook connections can explain a lot about a user's social life, her professional network may require an analysis of LinkedIn connections and her information consumption practices might be better explained by looking at her Twitter network. In addition, interesting patterns may emerge from the analysis of the dependencies between different combinations of these so-called network layers.
This tutorial will review the main theoretical models, data gathering methods and analytical tools to deal with multiple networks and to understand how a multi-layer network perspective may change our knowledge of user behaviors. Multiple online network analysis is a recent and growing field, with long-standing theoretical bases rooted in classical sociological analysis and multiplex social network analysis methods. As such, it presents numerous research opportunities both for experienced researchers and young academics looking for a field of specialization.Bio:
Matteo and Luca are principal investigators of national research projects on multiple network modeling and mining and are also active as organizers of international events on this topic, including the forthcoming Symposium on Multiple Network Modeling and Mining.
Presenters: Muhammad Aurangzeb Ahmad and Jaideep Srivastava
Massive Online Games (MOGs) refer to massive online persistent environments (World of Warcraft, EVE Online, EverQuest etc) shared by millions of people. In general these environments are characterized by a rich array of activities and social interactions with a wide array of behaviors e.g., cooperation, trade, quest, deceit, mentoring etc. Such environments allow one to study human behavior at a level of granularity where it was not possible to do so previously. Given the challenges associated with analyzing this type of data traditional techniques in data mining and social network analysis have to be extended with insights from the social sciences. The tutorial will cover predictive and generative models in the study of MOGs. Additionally we will cover some SNA techniques which are more appropriate for MOGs given the multi-dimensionality of the data (P*/ERGM Models, IR Based Network Analysis, Hypergraph based Techniques, Coextensive Social Networks etc). Based on our published work in this area we also describe the various ways in which MOGs exhibit similarities to the real world e.g., economic behaviors, clandestine behaviors, mentoring etc). Lastly we describe the scope and limitations of analysis of MOGs based on limitations in data collection, availability and ethical concerns. An overview of commercial applications is also given.Bio: