Eears (earthquake alert and report system) a real time decision support system for earthquake crisis management @ KDD'14

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EARS (Earthquake Alert and Report System)- a Real
Time Decision Support System for Earthquake
Crisis Management
2014/3/30(Mon.)
Chang Wei-Yuan @ MakeLab Lab Meeting
Marco Avvenuti
KDD‘14

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Outline
n Introduction
n Design and Implement
n Data Acquisition
n Data Filtering
n Event Detection
n Damage Assessment
n Testing and Results
n Conclusion
n Thought
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Introduction
n Social Media is the most effective,
sophisticated and powerful way to
gather preferences, tastes and activities
of groups.
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Introduction
n Emergency Management is a promising
field of application for social sensing.
n use spontaneous reports from social network
as our source of information
n analysis messages to quickly obtain details
of the impact of the event
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Introduction
n Goal：
n Development of a real time decision support
system for earthquake crisis management
n detection, alerting and assessment of the
consequences of earthquakes
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Design And Implement
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Data Acquisition
n Data collection
n completeness and specificity
n Initial set of 9 keywords
n progressively restricted to final set of 2
keywords
n Source: Twitter
n 1.5 million tweets
n 330,000 accounts
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Data Filtering
n Noise in collected data
n performs data filtering to find an ongoing
seismic event in 2 steps
n Step1: Pre-filtering
n Step2: Classifier filtering
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Data Filtering
n Noise in collected data
n performs data filtering to find an ongoing
seismic event in 2 steps
n Step1: Pre-filtering
n discard tweets that clearly do not refer to
an ongoing seismic event
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Data Filtering
n Noise in collected data
n performs data filtering to find an ongoing
seismic event in 2 steps
n Step1: Pre-filtering
n discard tweets that clearly do not refer to
an ongoing seismic event
n official news
n retweets / replies
n fakes / spams / bots
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Data Filtering
n Noise in collected data
n performs data filtering to find an ongoing
seismic event in 2 steps
n Step2: Classifier filtering
n infer the class of a tweet starting from a trained
model
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Data Filtering
n Noise in collected data
n performs data filtering to find an ongoing
seismic event in 2 steps
n Step2: Classifier filtering
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Data Filtering
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Event Detection
n The detection of an event is triggered by
an exceptional growth in the frequency
of the messages
n A burst is defined as a large number of
occurrences within a short time window.
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Burst Detection Algorithm
n The detection of a burst is based on the
calculation of the frequency of
messages in a short-term time window.
n A burst is detected when such frequency
exceeds a given threshold.

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Damage Assessment
n Damage assessment is the process that
allows emergency management to
determine the impact and the
consequences.
n Typically visiting the location of the event.
n Every new message is associated to the
event and contributes to the creation of
a corpus of reports.
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Chronological summary of the
events
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EARSINGV
n Red: not detected by EARS
n Orange: not yet confirmed by INGV
n Green: detected by EARS and confirmed by
INGV.

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Damage Assessment
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Testing and Results
n The evaluation dataset consists of all
the messages collected by EARS over a
70 days period.
n from 2013-07-19 to 2013-09-23.
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Testing and Results
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n True Positives (TP), events detected by the system and
confirmed by INGV;
n False Positives (FP), events detected by the system, but
not confirmed by INGV;
n False Negatives (FN), events reported by INGV but not
detected by the system.

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Testing and Results
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Conclusion
n The proposed system can clearly
provide useful information on the
consequences of seismic events.
n This paper used technical solutions for
the most relevant issues which are not
fully addressed in similar words.
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Thanks for listening.
2014 / 3 / 30 (Mon.) @ MakeLab Group Meeting
v123582@gmail.com