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DTSTART:20171105T020000
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UID:calendar.317436.field_event_date.0@www.wright.edu
DTSTAMP:20260219T215151Z
CREATED:20180416T173151Z
DESCRIPTION:Committee:  Drs. Soon Chung\, Advisor\, Nikolaos Bourbakis\, an
 d Vincent Schmidt (WPAFB)ABSTRACT:Although Twitter has been around for mor
 e than ten years\, crisis management agencies and first response personnel
  are not able to fully use the information this type of data provides duri
 ng a crisis or a natural disaster. This paper addresses the clustering and
  visualizing social media data by textual similarity\, rather than by only
  time and location\, as a tool for first responders. This paper presents a
  tool that automatically clusters geotagged text data based on their conte
 nt and displays the clusters and their locations on the map. It allows at-
 a-glance information to be displayed throughout the evolution of a crisis.
   For accurate clustering\, we used the silhouette coefficient to determin
 e the number of clusters automatically.  To visualize the topics (i.e.\, f
 requent words) within each cluster\, we used the word cloud. This tool cou
 ld be easily used by first response and official management personnel to q
 uickly determine when a crisis is occurring\, where it is concentrated\, a
 nd what resources to best deploy to stabilize the situation.
DTSTART;TZID=America/New_York:20180426T133000
DTEND;TZID=America/New_York:20180426T153000
LAST-MODIFIED:20180416T180909Z
LOCATION:304 Russ Engineering
SUMMARY:Masters Thesis Defense “Content-based Clustering and Visualization 
 of Social Media Text Messages” By Sydney Barnard
URL;TYPE=URI:/events/masters-thesis-defense-%E2%80%9C
 content-based-clustering-visualization-social-media-text-messages%E2%80%9D
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