Understanding Social Events: Investigating Twitter for Event Detection and Mobility Analysis
Interactive Social Media platforms offer a tremendous amount of voluntarily, user-generated content (Flickr, Twitter, Blogs, OpenStreetMap, etc.). In today’s information society and knowledge economy these portals provide a valuable resource for diverse application domains. The enormous potential of this voluntarily geographic information (VGI) contributed by the masses of volunteers (crowd) is increasingly recognized, but in many areas, especially in science, it is not utilized to its full potential. There are several unsolved issues that arise from these rapidly increasing, very dynamic and highly heterogeneous data streams of content created by users. Addressing these issues has the goal to automatically assess and develop this new type of poorly structured data for different application domains, in particular, to infer new information. Our research objective is to develop novel methods and approaches towards the quality-oriented analysis and exploration of crowd-sourced social-media data. Real-time social sensor data could be used directly or indirectly to derive spatiotemporal human mobility- and motion patterns on a city scale level. Enrico’s research is focused on the overall question how these spatiotemporal patterns in ubiquitous sensor networks and heterogeneous data streams can be explored, extracted, validated and aggregated in order to be able to sense urban geo-processes and to gain knowledge about urban dynamics. The identification of mobility hubs and the extraction of movement trajectories could be used to understand, enrich and improve mobility and intelligent transportation systems (ITS). Mass-convergence events like music festivals or demonstrations consist of numerous sub-events at multiple scale levels. In order to “peel these events like an onion”, René analyzes Twitter Tweets harvested during four major music festivals in England. He combines approaches from various disciplines to consider all available information layers (e.g. position, text …) from Twitter Tweets. By following this comprehensive approach and considering spatial and temporal scale as key concepts, he conducts an in-depth analysis of VGI. The benefit of this research is to provide stakeholders with near-real-time information about time-critical situations. Furthermore, it allows analyzing social aspects of such events.
Enrico is a PhD student in the Department of GIScience at Heidelberg University. He received his Diplom in Geography at the University of Leipzig (Germany), specializing in the field of Geoinformatics and transport geography. From 2009 till 2012 he was employed as a GIS researcher working for a traffic engineering company in Salzburg. His main focus was based on the processing and development of travel time models based on GNSS data and the use of community-based routable graphs in OpenStreetMap. His recent work deals with the spatiotemporal extraction of human mobility pattern from twitter data.
René is also a PhD student in the GIScience Research Group at Heidelberg University. He received his master’s degree in Geoinformatics at the University of Osnabrück in northern Germany. During his studies he specialized in OGC web services and web mapping applications. René is currently working on scale-dependent event detection from Twitter Tweets. Furthermore, he is an affiliate of the Botanical Garden of the University of Osnabrück, where he aims to transfer GI-technologies into the field of Botany.