Mining and Visualizing Research Networks using the Artefact-Actor-Network Approach

Reinhardt, W., Wilke, A., Moi, M., Drachsler, H., & Sloep, P. B. (2012). Mining and Visualizing Research Networks using the Artefact-Actor-Network Approach. In A. Abraham (Ed.), Computational Social Networks. Mining and Visualization (pp. 233-268). Springer. Also available at http://www.springer.com/computer/communication+networks/book/978-1-4471-4053-5Virtual communities are increasingly relying on technologies and tools of the so-called Web 2.0. In the context of scientific events and topical Research Networks, researchers use Social Media as one main communication channel. This raises the question, how to monitor and analyze such Research Networks. In this chapter we argue that Artefact-Actor-Networks (AANs) serve well for modeling, storing and mining the social interactions around digital learning resources originating from various learning services. In order to deepen the model of AANs and its application to Research Networks, a relevant theoretical background as well as clues for a prototypical reference implementation are provided. This is followed by the analysis of six Research Networks and a detailed inspection of the results. Moreover, selected networks are visualized. Research Networks of the same type show similar descriptive measures while different types are not directly comparable to each other. Further, our analysis shows that narrowness of a Research Network's subject area can be predicted using the connectedness of semantic similarity networks. Finally conclusions are drawn and implications for future research are discussed

Social Media Monitoring and Analysis: Multi-domain Perspectives

Social Media Platforms, such as Facebook or Twitter, are part of everyday life as powerful communication tools. They let users communicate anywhere-anytime, improve their own public image and readily share information. For this reason, a growing number of individuals such as professionals as well as companies have opened an account in one or more Social Media platforms. Due to the widespread use and growing numbers of users, a huge amount of data is generated every day. This information may play a crucial role in various decision-making processes. In this setting, research topics connected to monitoring and analysis of Social Media data are becoming increasingly important. The present work stems from data collection methodologies from different Social Media sources. It introduces the problems involved in storing semi-structured data, and in possible information gaps due to privacy policies. These facets are described according to the application domain as well as the Social Media platform. Subsequently, a theoretical generic architecture for handling data from Social Media sources is presented. We present three simplified versions of this architecture in three different domains: Online Reputation, Social Media Intelligence, and Opinion Mining in tourism. In the last part of the work, we introduce Social Media Analysis in these three domains. For the first, we present the project SocialTrends, a web application able to monitor “public” people on Facebook, Twitter, and YouTube. In the second, we introduce an innovative approach for measuring the interactions between users in public spaces such as Facebook (public-by-design). Finally, we present Tour-pedia, a web application that displays a sentiment map of tourist locations in several cities according to different categories (accommodation, restaurants, points of interest and attractions)