When a natural disaster happens, emergency management and civil defense agencies need to determine the impact and circumstance of the event, and this process is often called situation awareness. Within the massive amount of information from multiple sources, only a small proportion is directly related to situation awareness. Therefore, decision-makers need to determine the data sources (that are more important and helpful than others) and ensure the collection of timely and reliable information. As one source of information, social media has contributed significantly to geographic situation awareness during the past decade. Along with the benefit, however, social media data contains a significant amount of noise, i.e., tweets that are not contributing to disaster management, tweets that are generated by a robot or advertisement account, or tweets posted with questionable provenance (fake news).
This session will focus on the idea of collecting useful information, filtering out interference from crowdsource data and making social media better serve GIS research.
|Presenter||Mark Butman*, Courage Services, Inc., Mapping Ebola Diffusion in East Africa: Geospatial Analysis and Social Media Monitoring||20|
|Presenter||Xujiao Wang*, Texas State University, Yihong Yuan, Texas State University, Measuring User Activity Similarity from Location-Based Social Media: A Spatial-Temporal Approach||20|
|Presenter||Chenxiao (Atlas) Guo*, University of Wisconsin-Madison, Qunying Huang, University of Wisconsin-Madison, Examining Spatiotemporal Pattern of Evacuation Behavior during 2017 Atlantic Hurricane Season||20|
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