IBM Japan, Ltd.

Tweeting about the Tsunami?
Mining Twitter for Informationon the Tohoku Earthquake and Tsunami

IBM Research – Tokyo
Akiko Murakami and Tetsuya Nasukawa© 2012 IBM Corporation

IBM Japan, Ltd.

The Tohoku Earthquake and Tsunami
 The earthquake and tsunami were the first large-scale natural disasters in Japan
since social media became widespread.
– Though the wired and mobile phone networks went down just after the accidents,
the Internet was widely accessible except in the worst-affected region.
– After recovery from power outages, social media played an important role inexchanging and gathering information.
Trains areavailable?
We need
XXX…

Social media played a role ofsensors to get peoples’ thought
in disaster areas2

2

how people are feeling and learn
about their thoughts, demands,
and opinions by social medias© 2012 IBM Corporation

IBM Japan, Ltd.

What’s going on? - Mining Twitter for Information
 We applied Text Mining techniques for Microblog service (Twitter) data
Need
medicine…

Analysis to extract
knowledge(ex. Shortage things)• Finding Shortages of Supplies• Identifying Types of Fraud
Can not buy
food, water,etc…
33

Input tosocial medias(Twitter)© 2012 IBM Corporation

IBM Japan, Ltd.

Data Sets – Textual data from social media

Data set
From the 13th to the 28th in March
1,135,495 tweets.
(For preliminary study
From the 13th to the 16th of March 2011
Data

Keyword search API

280,429 tweets )Could not crawl data everyday
due to planed power outage•“地震” (Earthquake)•“被災” (Disaster-affected)•hash tags such as “#jisin”
or “#jishin” (Earthquake)
After March 14th•“原発” (Nuclear Plants)
44* Most of the tweets were written in Japanese.
© 2012 IBM CorporationIBM Japan, Ltd.

Text Miner - Powerful Views to Find Insight

Text Mining Tool
TAKMI – Text Mining and Knowledge Mining(Product name: IBM Content Analytics)
55© 2012 IBM Corporation

IBM Japan, Ltd.

Relevant nouns in tweets contains “Shortage(足りない)”
Mining Shortage of Supplies without analytic resources

Noun

66

Correlation

Frequency

personnel
resources

48.6

35

supplies

12.2

21

consideration

7.8

25

heating oil

7.3

30

provision

6.0

32

medicine

5.7

25

electric power

5.4

63

food

4.631
© 2012 IBM Corporation

IBM Japan, Ltd.

Relevant nouns in tweets contains “Shortage(足りない)”
Mining Shortage of Supplies with a pattern dictionary
We prepared a syntactic pattern dictionary to identify things that were in short supply
Extract <Nouns> with syntactic patterns
Ex. <Noun> が 買えない (cannot buy <Noun>)<Noun> が 売り切れ (<Noun> is sold out)

personnel resources
supplies
consideration
heating oil
provision
medicine
electric power
food
without pattern dictionary
77waterbatteryricegas
toilet paper
milk…

with a pattern dictionary© 2012 IBM Corporation

IBM Japan, Ltd.

Trend of shortages extracted syntactic pattern dictionaries- People bought out things just after the disaster time Gasoline
Just after the disaster time

BatteryRotation Power Outage

Water
After tap watercontamination
report88© 2012 IBM Corporation

IBM Japan, Ltd.

Trend of shortages extracted syntactic pattern dictionaries- People bought out things just after the disaster time Fermented Soybeans

Fermented foods did restart their production
after the end of rotational power outage

YogurtCigarette

99

Cigarette did not restart their production for a long time

© 2012 IBM Corporation

IBM Japan, Ltd.

Summary Text mining tools showed the effectiveness for mining twitter
data at the time of disaster– Finding shortage of things– Knowing the trend of shortages
Future Works Social media (ex. Twitter ) data includes other richinformation such asAuthors, replies and reshare (retweet)
 Combine out textual analytics and network analytics
techniques may more reveal human behaviors and needs
when disaster strike1010© 2012 IBM Corporation