Visual Analytics of Social Media for Situation Awareness
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Beschreibung
This thesis illustrates how social media can be used to support situation awareness. It presents novel techniques to address challenges of data access, context, semantic complexity, and scalability.
With the emergence of social media services like Twitter or Instagram the internet recently grew into a continuous stream of knowledge, observations, thoughts, and situation reports. This change offers completely new possibilities for application domains that rely on good situation awareness, such as disaster management, emergency response, or disease control. This thesis identifies four visual analytics requirements that have to be addressed to create situation awareness from social media: Access to data, visualization of context, coping with semantic complexity, and scalable processing. Based on core ideas of visual analytics, this work contributes three distinct techniques that allow to tackle access, context, and complexity, as well as a prototypical implementation that combines all of them and allows scalable processing of the data. The thesis furthermore presents an overarching analytics model, which integrates all solutions and relates their distinct capabilities. All of the presented methods combine the analytical power of data mining and information retrieval with the distinct capabilities of human cognition by means of highly interactive visual interfaces. It is demonstrated how the aspects of user-driven detection and data-driven discovery distinctly align with supervised and unsupervised methods in machine learning. From the lessons learned it is conclusively shown that visual configuration and steering of supervised classification on one hand and the enhancement of visual interfaces through unsupervised clustering on the other hand are two complementary concepts embedded at the very heart of visual analytics. The presented overarching analytics model could thus help to further enhance previous definition approaches and ostensive conceptions existing in the field. Attention: Please be aware that this is an unmodified doctoral dissertation originally intended for academic use.
Autor*in
Dennis Thom
Dennis Thom received his diploma and PhD in computer science from the University of Stuttgart. Since 2011 he has been employed there as an assistant researcher at the Visualization and Interactive Systems Institute. His research interests comprise the fields of information visualization, visual analytics, machine learning, text data mining, and social media. He has (co-)authored more than 30 peer-reviewed publications in international journals and conference proceedings.
Themen in »Visual Analytics of Social Media for Situation Awareness«
visual analytics data visualization social media twitter facebook instagram api computer web informatik