DocumentCode :
2796854
Title :
Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition
Author :
Junghoon Chae ; Thom, D. ; Bosch, Harald ; Yun Jang ; Maciejewski, Ross ; Ebert, David S. ; Ertl, Thomas
fYear :
2012
fDate :
14-19 Oct. 2012
Firstpage :
143
Lastpage :
152
Abstract :
Recent advances in technology have enabled social media services to support space-time indexed data, and internet users from all over the world have created a large volume of time-stamped, geo-located data. Such spatiotemporal data has immense value for increasing situational awareness of local events, providing insights for investigations and understanding the extent of incidents, their severity, and consequences, as well as their time-evolving nature. In analyzing social media data, researchers have mainly focused on finding temporal trends according to volume-based importance. Hence, a relatively small volume of relevant messages may easily be obscured by a huge data set indicating normal situations. In this paper, we present a visual analytics approach that provides users with scalable and interactive social media data analysis and visualization including the exploration and examination of abnormal topics and events within various social media data sources, such as Twitter, Flickr and YouTube. In order to find and understand abnormal events, the analyst can first extract major topics from a set of selected messages and rank them probabilistically using Latent Dirichlet Allocation. He can then apply seasonal trend decomposition together with traditional control chart methods to find unusual peaks and outliers within topic time series. Our case studies show that situational awareness can be improved by incorporating the anomaly and trend examination techniques into a highly interactive visual analysis process.
Keywords :
Internet; data visualisation; graphical user interfaces; interactive systems; social networking (online); time series; Flickr; Internet user; Twitter; YouTube; abnormal event detection; abnormal topics examination; abnormal topics exploration; control chart method; interactive social media data analysis; interactive visual analysis process; latent Dirichlet allocation; local event; seasonal trend decomposition; seasonal-trend decomposition; situational awareness; social media service; space-time indexed data; spatiotemporal data; spatiotemporal social media analytics; time-stamped geo-located data; topic time series; visual analytics approach; visualization; volume-based importance; Data mining; Earthquakes; Educational institutions; Media; Spatiotemporal phenomena; Time series analysis; Twitter; H.3.3 [Information Storage and Retrieval]; H.5.2 [Information Interfaces and Presentation]: User Interfaces — GUI; Information Search and Retrieval — Information filtering; relevance feedback;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4673-4752-5
Type :
conf
DOI :
10.1109/VAST.2012.6400557
Filename :
6400557
Link To Document :
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