IEEE Technology and Society Magazine - December 2017 - 41

LEADING EDGE

Asslam Umar Ali Robert Ogie

Social Media
and Disasters

nformation generated on social media
sites such as Twitter, Facebook, Flickr,
and Instagram are
fast becoming powerful and ubiquitous new sources of time-critical
data needed to aid decision making
during extreme weather events and
emergency situations [1], [2]. The
large-scale spatio-temporal data
harvested from these social media
sites during past events such as
the 2010 earthquake in Haiti [3], the
2011 tsunami in Japan [4], and the
2010-2011 Queensland, Australia,
flood [5] attest to the fact that realtime crowd-sourced disaster information has huge potential to enable
decision makers and first responders to detect disruptive events quickly, gain situational awareness, and
respond swiftly to unfolding emergency situations.
This type of content generated
on social media sites using mobile
devices with geospatial information
has been termed Volunteered Geographical Information (VGI) [2]. As
promising as this VGI data collection
initiative may appear, the process
involved in harnessing social media
data for disaster management is not
straightforward; it often requires a
carefully designed solution.
One of such solutions is the opensource and cloud-based Cognicity
software that was developed as part
of the PetaJakarta.org project [6].

I

Highlighting Some Wicked Problems
The PetaJakarta.org project is an
urban data collection initiative developed by the SMART Infrastructure
Facility (University of Wollongong,
Australia) in collaboration with the
Jakarta Emergency Service (BPBD
DKI Jakarta) and Twitter Inc. As part
of the PetaJakarta data collection
campaign, Twitter users in the city
of Jakarta are encouraged to share
tweets about flood conditions in
their localities, preferably ones with
embedded photos and text descriptions of water level. Cognicity then
parses through the incoming torrent of tweets, filtering flood-related
tweets by the keyword, "flood," or
"banjir" in Bahasa, an Indonesian
language. The system differentiates
geo-located and non-geo-located
tweets and also enables a two-way
communication, wherein users can
confirm if they are actually reporting
about an ongoing flood situation.
After gathering and sorting the
data, the confirmed geo-located
tweets are used to generate a near
real-time map of flood conditions in
various parts of the city [7]. This publicly available city-scale flood map

Digital Object Identifier 10.1109/MTS.2017.2763450
Date of publication: 5 December 2017

DECEMBER 2017

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IEEE TECHNOLOGY AND SOCIETY MAGAZINE

guides citizens to navigate safely
through the city and also enables
emergency services (BPBD DKI Jakarta) to plan and coordinate operational activities. While this project
has recorded huge success over the
last few years of operation, there
remain some "wicked problems"
that need to be addressed go ing forward.
One such issue is how to improve
the objectivity of data contributed by
users. Users' opinions about flood
conditions and the way they report
those opinions can vary markedly,
depending on factors such as level
of education, motivation, device
functionalities, personality, system
constraints (e.g., Twitter 140-character limit), etc. This is a major problem when trying to estimate water
level information needed for flood
maps. For example, what could be
the most objective way for a user to
accurately report the flood height in
a given location? Clearly, measurements associated with user's height
(e.g,. water at knee level) are highly
(continued on page 43)

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http://www.PetaJakarta.org http://www.PetaJakarta.org
Table of Contents for the Digital Edition of IEEE Technology and Society Magazine - December 2017
