Approximations to geolocation of disaster related tweets

Date of Award

2017

Document Type

Thesis

Degree Name

Master of Science in Computer Science, Straight

Department

Information Systems & Computer Science

First Advisor

De Vera, Jose Alfredo A., M.S

Abstract

The use of tweets as information aid during disasters has been limited by the lack of location information in majority of the tweets. This study created two algorithms to approximate tweet location based on the text content of the tweets. The first algorithm used machine learning algorithms to predict the distance of a tweet from the eye of the typhoon and the disaster affected area. The second algorithm employed semantic modeling and comparison to predict the location of a tweet as latitude-longitude coordinate. The results of these studies show that temporal factors are important in creating more accurate location approximation models. Models that predict a tweet's relative distance to the affected area have also been shown to be more effective than models that predict relative distance to the eye of the typhoon.

Comments

The C7.R685 2017

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