Evaluating Disaster Time-Line from Social Media with Wavelet Analysis
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Abstract
—For over a decade, social media has proved to
be a functional and convenient data source in the Internet of
things. Social platforms such as Facebook, Twitter, Instagram,
and Reddit have their own styles and purposes. Twitter, among
them, has become the most popular platform in the research
community due to its nature of attracting people to write
brief posts about current and unexpected events (e.g., natural
disasters). The immense popularity of such sites has opened a
new horizon in ‘social sensing’ to manage disaster response.
Sensing through social media platforms can be used to track
and analyze natural disasters and evaluate the overall response
(e.g., resource allocation, relief, cost and damage estimation).
In this paper, we propose a two-step methodology: i) wavelet
analysis and ii) predictive modeling to track the progression of a
disaster aftermath and predict the time-line. We demonstrate that
wavelet features can preserve text semantics and predict the total
duration for localized small scale disasters. The experimental
results and observations on two real data traces (flash flood in
Cummins Falls state park and Arizona swimming hole) showcase
that the wavelet features can predict disaster time-line with an
error lower than 20% with less than 50% of the data when
compared to ground truth.