Visual analytics of user influence and location-based social networks

Abstract

Socialmedia have transformed the way people express opinions, react to evolving and emergent events, and share their whereabouts. When an event occurs, information generated by users who witness or engage in it can provide first-hand accounts and updates. This information is propagated in the online social networks and triggers reactions from other users. Identifying influential users, monitoring the interaction between users, and analyzing information diffusion in social media canimprove situational awareness in a crisis situation, and provide significant and reliable information for emergency management. Yet, inferring actionable informationfrom raw social media data is not straightforward. The large volume of data and their multiple dimensions makes this process extremely difficult. The real time streaming nature of social media data introduces additional challenges. Reliance only on fully automated methods with minimum human intervention is not suitable while working with such datasets. Besides that, analysts working on such problems often need to look into the contextual evidence that could help them accept or reject certain hypothesis. Such contextual information could be provided only if application framework supports interactive exploration, querying, and visual feedback. In this chapter, we introduce our suite of interlinked visual analytics tools that attempt to overcome these issues.