In this session, we start with Twitter data from a past disaster and use Amazon Comprehend to discover insights and relationships. Finally, we use Amazon SNS to notify iOS, Android, and Fire OS-based mobile devices.

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Machine Learning for Improving Disaster Management and Response
Doina Caragea
Professor of Computer Science
Kansas State University
W P S 3 1 3
Sanjay Padhi
AWS Research Initiatives, WWPS
Amazon Web Services

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Acknowledgements
BIGDATA: IA: Collaborative Research: Domain Adaptation
Approaches for Classifying Crisis Related Data on Social Media

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Deadly disasters happen all the time

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Year 2017―the costliest for natural disasters in US

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9-1-1 lines can be overwhelmed

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Social media to the rescue

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Responders faced with information overload
Direct Twitter search: noisy, non-
relevant tweets retrieved
Keyword-based search:
“harvey hurricane”, #harveyhurricane
Location-based search
postings containing geographical
coordinates inside the affected areas
Manual selection: time consuming

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Streaming
Crisis Data
(Twitter)
Human
Analyst
Amazon Machine
Learning
(Amazon ML)
Historical Crisis Data and Models
Crisis Affected
Community
Response
Organizations
Unlabeled
Unlabeled
Unlabeled
Labeled
Labeled
Labeled
911
Dispatcher

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Methodology
Data Collection
JSON tweets
Data Extraction
Tweet id, create time, text
Data Processing
Stop words, special
characters, URLs, Emails
Topics Modeling
Streaming Corpus
Latent Dirichlet Allocation
Analysis
Preparedness, During
Hurricane, Aftermath
Hurricane timeline

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• Use Twitter Streaming API to crawl tweets posted during crisis events
• Parse the tweet JSON objects to extract tweet text, hashtags, media
information, user information, and geo-location (when available)
• Perform text classification, natural language processing and text analytics on
the tweet text
Data collection and analysis

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Tweet classification

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Classes of machine learning algorithms
Supervised learning [Imran et al., 2013; Ashktorab et al., 2014; Caragea et al., 2014; Imran et al.,
2018]
• Labeled tweets needed, but not readily available for an emergent disaster
Domain adaptation [Li et al., 2015; Li et al., 2017, Alam et al., 2018, Mazloom et al., 2018]
• Knowledge from a prior source disaster is transferred to a target disaster
Unsupervised learning, e.g., topic modeling [Resch et al., 2017]
• Topic modeling can help associate topics/categories with tweets

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Large road signs down over I-37 near Corpus. #ksatwx #harvey
#HurricaneHarvey At least one dead in Texas, more casualties feared
Currently stuck on Monroe.... R.I.P my truck... #HurricaneHarvey
I’ve got a water stain the size of Texas on my shirt so that’s cool
10/30-11/2 Water Infrastructure Conference happening in #Houston
Relevant
Relevant
Relevant
Irrelevant
Irrelevant

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Relevant
Irrelevant
Classifier
Large road signs down over I-37 near Corpus. #ksatwx #harvey
#HurricaneHarvey At least one dead in Texas, more casualties feared
Currently stuck on Monroe.... R.I.P my truck... #HurricaneHarvey
I’ve got a water stain the size of Texas on my shirt so that’s cool
10/30-11/2 Water Infrastructure Conference happening in #Houston
Relevant
Relevant
Relevant
Irrelevant
Irrelevant

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Classes of machine learning algorithms
Supervised learning [Imran et al., 2013; Ashktorab et al., 2014; Caragea et al., 2014; Imran et al.,
2018]
• Labeled tweets needed, but not readily available for an emergent disaster
Domain adaptation [Li et al., 2015; Li et al., 2017, Alam et al., 2018, Mazloom et al., 2018]
• Knowledge from a prior source disaster is transferred to a target disaster
Unsupervised learning, e.g., topic modeling [Resch et al., 2017]
• Topic modeling can help associate topics/categories with tweets

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Labeled Source Data
Unlabeled Target Data
Classifier for Target

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Classes of machine learning algorithms
Supervised learning [Imran et al., 2013; Ashktorab et al., 2014; Caragea et al., 2014; Imran et al.,
2018]
• Labeled tweets needed, but not readily available for an emergent disaster
Domain adaptation [Li et al., 2015; Li et al., 2017, Alam et al., 2018, Mazloom et al., 2018]
• Knowledge from a prior source disaster is transferred to a target disaster
Unsupervised learning, e.g., topic modeling [Resch et al., 2017]
• Topic modeling can help associate topics/categories with tweets

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Topic modeling using Latent Dirichlet Allocation (LDA)
A Document is a Mixture of Topics

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Topic modeling using Latent Dirichlet Allocation (LDA)
LDA finds topics in a collection of documents/tweets LDA tags each document/tweet with one or more topics
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Damage Donations Recovery Storm Utilities
Document: topic distribution
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
area flood heavy morning thursday tropical wind
Topic: word distribution

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Amazon Comprehend workflow

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How can Amazon Comprehend help disaster response?
Identify the language of a tweet
Identify popular key phrases to facilitate keyword search
Identify entities in actionable tweets
• Determine: who (person, organization), where (location), when (date), etc.
Identify the sentiment of the tweets to enhance situational awareness
Build custom classifiers to categorize tweets
Identify topics in a collection of tweets to categorize tweets, find trends and patterns

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“We're at 11115 Sageview in Houston. Water is swallowing us up. Please
try calling 911 for rescue. Please send help”
“People dying, widespread destruction #HurricaneMaria #HurricaineIrma
and all top stories are @realDonaldTrump. Sad. Very sad. Disappointing”
“Awful seeing whats going on in the Caribbean. THE most resolute people
but these countries wont recover w/o sufficient aid #HurricaneMaria”
“Escaping Hurricane Irma and driving to South Bend with @emhen10 and
our 3 furry kids... #GoIrish #BeatBulldogs https://t.co/DFc0LeaOIt”

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Topic modeling

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Amazon Comprehend topic modeling output

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Example: topics
Hurricane degenerates Hurricane regenerates
Harvey degenerates into tropical wave
RT Hurricane Hunter plane finds that the
remnants of Harvey have not regenerated
into tropical cyclone
RT Harvey showing signs of regeneration over western Caribbean Sea
amp will produce heavy rainfall storms this week hit
RT Active tropics continue redvlpmt of Harvey likely as well as
the potential for two new tropical cyclones next days on

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Amazon Comprehend custom classifiers

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Amazon Comprehend custom classifiers - Output

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Using Amazon Comprehend results to get aggregate statistics

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Using Amazon Comprehend results to determine frequent entities

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Using locations identified by Amazon Comprehend to track hurricane path
Source: Wikipedia
Source: Weather Channel

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Using locations identified by Amazon Comprehend to track hurricane
path
Source: Weather Channel

40. Thank you!
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Doina Caragea – dcaragea@ksu.edu
Sanjay Padhi – sanpadhi@amazon.com

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