~how twitter used during disaster~syrinx.q.t.u-tokyo.ac.jp/tori/lecture/2015/resilience...social...

Social Network for Resilience~How Twitter used during disaster~

Department of Systems Innovation

Graduate School of Engineering

Fujio Toriumi

レジリエンスエ学特論E

2014 Advanced Lecture on Resilience Engineering

Today’s Topics

• How people use Social Media under the

disaster situation?

– From Twitter Big Data Analysis

• Who use the Twitter

• Can people use specific features of Social Media?

• How information diffused on Social Media

– Network Structure

– Diffusion Capability

Large Scale Disasters

• Large disasters in last ten years

– The Indian Ocean off Sumatra (2004)

– Hurricane Katrina (2005)

– Sichuan earthquake(2008)

– Chile earthquake (2010)

– Great East Japan Earthquake (2011)

– Hurricane Sandy (2012)

– Haiyan Typhoon, Philippines(2013)

Collecting Information under

Disaster Situation

• Important to save lives

– For Victims

• Shelters

• Dangerous points, …

– For Rescuers

• The victim locations

• The availability of supplies, …

How to collect information?

• Information from mass media

– Too much information, but Limited time

– Blackout

– No route to carry Newspapers

– Difficult to collect desired information

• Cellphones with internet, Wifi

– High failure resistance

Collect Information from WEB

• Social media is useful

– Twitter, Facebook, U-Stream and so on

– Accessible by mobile tools

The Great East Japan Earthquake

• Earthquake

– Magnitude 9.0

– 14:46 11th March, 2011

• Tsunami

– Height : 5-20m height (Max Run-up : 38.9m)

– Area Flooded : 507km2

• Fukushima Nuclear Accident

– Loss of power caused by Tsunami

– Meltdown : Units 1, 2, and 3

Usage of Japanese Social Media

MicroBlog

Social Media

How Twitter used in Japan

• Users : 12,820,000 (Feb, 2011)

• Daily average tweets : 18,000,000

• Twitter users increases drastically during

the disaster

– 63.9% users answers that it is useful to collect

information on Twitter

– 34.9% on Facebook

Mobile Marketing Data Labo. (2011)

Before and After the Disaster

• Disaster changes Twitter

– People behaviors, information

– Network structures

Today’s Topic


disaster situation?




• How information network changes?



Data Set

• Date

– 7th – 23th March, 2011

• The Great East Japan Earthquake occurred at 11th

• Number of Tweets

– 363,435,649 (7-80% of all Japanese tweets)

• Number of Retweets

– 29,245,815

• Number of Users

– 2,727,247

Average Number of Tweets

in each Minutes

(Before the disaster)

Lunch

Many botsPeaks found at night

Number of Tweets

The Great East Earthquake M9.0

M6.6

M6.1M7.2

Increasing rate of Tweet (minute)

• Normalized by average number of Tweet at same time before the disaster– Extracting peaks

• Peaks found at afterquakes occurrence– No peak found at Nuclear accident

M6.0

Nuclear Accidents in Fukushima

Today’s Topic


disaster situation?







Changes in Number of Tweets

• Before disaster vs After disaster (4days)

– Before Disaster ：7th to 10th March, 2011

– During Disaster ：11th to 14th March, 2011

Casual users

increase number of

tweets

Heavy users

decrease number of

tweets

Over 10 tweets per days

• Number of casual users do not change

before and after the disaster

The Great

East Japan Earthquake

Over 400 Tweets per days

• Number of heavy users decreased

The Great

East Japan Earthquake

Changes in Number of Posted

Tweets• People likely to spread information more

frequently during a disaster

– To share many important information

– Why did active users reduce their tweets?

• Who is the frequent tweet user?

– 400 Tweet per day = 1 Tweet per 3.6 minutes

Number of bots

• The users with large amount of tweets

=> Likely to be bots

– Bot

• Computer programs which post tweets

automatically

• Hypothesis : the number of bots

decreased during the disaster

– Check whether active bots decreased

Tweets of bots

• The number of active bots dropped

• Stop to provide unnecessary information

– Most of bots post jokes and advertisements

– Without forcing

Today’s Topic


disaster situation?







Usage Analysis of

Twitter Systems• Systems used for information sharing

– HashTag : Information clustering & searching

– Reply :Communication

– Retweet : Information Diffusion

• Can people use specific features of Social

Media?

– How people use the features

– Can people start and master to use features

at the time of disaster?

Numbers and Types of

HashTags

Daily Top 5 HashTags

3月10日 3月11日 3月12日 3月13日 3月20日 3月23日

1 #followmejp #jishin #jishin #jishin #followmejp #followmejp

2#sougofollo

w#j_j_helpme

#prayforjapa

n

#prayforjapa

n#jishin

#sougofollo

w

3 #nowplaying #followmejp #nhk#save_ibara

ki

#sougofollo

w#nowplaying

4 #nicovideo#prayforjapa

n#anpi #anpi #nowplaying #nicovideo

5 #followme #jisin #jisin #nhk #sutadora #jishin

Rate of Tweet, Reply and

Retweet

Two types of communication

• Follower network

– Network created from follower-followee

relations

• Reply/Retweet with followers

– Communication with friends

• Reply/Retweet with non-followers

– Communication with non-friends

– Sharing information

follow

Replies with followers

• Private communication structures were not

changed

• Used replies to communicate with friends

Retweets with followers

• Reply information from non-friends

– Not private information

• Use retweets to share global information

– Required information changes

Usage of Hashtag

• Pre-HashTag User:

– Users who used HashTag before disaster

Usage of Reply

• Pre-Replayer:

– Users who used Reply before disaster

Usage of Retweet

• Pre-Retweeters:

– Users who used Retweet before disaster

Rate of Usage of Features

• Pre-Users : Used systems actively

• Non-PreUsers :

– Difficult to start using new features

Used in 11th Used before 24th

Hash TagPreUser 25.2% 84.7%

Non-PreUser 4.9% 34.3%

ReplyPreUser 52.3% 92.9%


RetweetPreUser 51.0% 90.8%


Today’s Topic


disaster situation?







How twitter network changes

before and after the disaster

• Information Diffusion Network

– Communication Network

• Connect link between nodes which used RT or Reply

– 7th March to 23rd March

• In-directed Network

Reply

Retweet

Models of Network

Before and After Disaster

Network Before DisasterNetwork After Disaster

Statistical Data of Networks

# of Nodes # of Links Avg. Degree Max Degree

7th March 1505772 7743168 5.14 5143

12th March 1734187 19286490 11.1 108297

23rd March 1738702 10051644 5.78 17700

• # of Nodes Increase

– Increase of Users

• # of Links Increase

– Increase of Communication

• Appearance of huge degree Node

– Center of information sharing

Rate of Over 1000 degree

Users

High Degree Users~Before the disaster~

• @youtube

– Account of Youtube

• @shuumai

– Free talk Bot

• @wwwwww_bot

– Joke bot

• @foursquare

– Account of Foursquare

High Degree Users

~After the disaster~

• @NHK PR

– Public account of NHK press agent

• @FDMA JAPAN

– The Fire and Disaster Management Agency

• @earthquake jp

– Emergency earthquake alert system

• @oohamazaki

– A user who developed web site of shelters in Tokyo

Changes in Roles of Twitter

• Main role of twitter and Hub Users

– Before Disasters• Communication

• Providing topics and communication

– After Disaster• Information sharing

• Providing information

• Role of Twitter

– Communication tool Information sharing tool

Today’s Topic


disaster situation?







Twitter networks desirable for

information diffusion?

• Difficult to compare

– Not only structures were change

Analyze how information diffused

on each networks

Information diffusion simulation

Information Diffusion Simulation

• Simulate Information Diffusion on Network

• Analyze the influence of network structures

– Focus on structures, not users

– Which kind of structure accelerate diffusion

• Use Independent Cascade model (IC model)

– Basic diffusion model

– Based on SIR model

Independent Cascade Model

• Status of Nodes

– Susceptible

– Information Sending

– Received

𝑃1

𝑃2

Susceptible

Information Sending

Received

Ability of Information Diffusion

(AID)

• Higher 𝐴𝐼𝐷 network

• Higher capability of information diffusion

Information

source 𝑣

success failure

Rate of users who

received information

𝜎(𝑣)

𝐴𝐼𝐷 =1

𝑁

𝑁

𝜎(𝑣)

N：Num of Users

Diffusion Simulation on networks

before/after the disaster• Purpose

– Analyze how information diffused on each networks from AID

• Method

– Information diffusion simulation on real networks

• Settings

– Use communication networks on Twitter• Directed Network

– Use networks created in 7th Mar, 2011 to 15th

Mar, 2011

Change in 𝐴𝐼𝐷

The

Disaster

Befor

eAfter

AID become higher after the disaster

9%

3%

Models of Network

Before and After Disaster

Lower AID Higher AID

How can we realize desirable

networks?

• Purpose

– Find the feature which have high influence to information diffusion

• Method

– Change each feature

– Analyze changes in AID

• Settings

– Use real network features

– Create 100 networks for each feature type

Network Indexes

• reciprocity 𝜌

• Transitibity 𝜏

• Assortativity 𝑟

• Determination

coefficient of power-

law

– In degree 𝑖𝑛𝑅2

– Out degree 𝑜𝑢𝑡𝑅2

• Reachability 𝛼

• Cyerosity 𝑐

• Node Assortativity

• Power Index

– In degree 𝑖𝑛𝛾

– Out degree 𝑜𝑢𝑡𝛾

Proposed Generalized

Network Growth Model

• To realize any types of Networks

– View point of network features

• Basic strategy

– Greedy growth model

– Target similarity evaluation model

Sample of Generated Networks

Information Diffusion Simulation

• Create many networks

– One target index with another fixed indexes

– Change target index and create various

networks

– Ex. High-Reciprocity network and Low-

Reciprocity network which has same other

network indexes

• Calculate AID with the network

– Correlation between AID and changed index

– Is higher reciprocity provides higher AID?

• Use Rank Correlation

Reciprocity Reachability Transitibity Cyerosity Assortativity0.176 0.848 -0.0173 0.205 -0.185

Correlation between AID and

features

Node Assortativity

Determination coefficient(Out)

Power Index(Out)

Determination coefficient(In)

Power Index(In)

0.967 0.0881 0.320 -0.0641 -0.165

• Use Rank Correlation

• High Reachability and High Node

Assrotativity

• High AID: Easy to diffuse information

Correlation between AID and

features

High Correlation


Node Assortativity


Power Index(Out)


Power Index(In)

0.967 0.0881 0.320 -0.0641 -0.165

Reachability

• Reachability: Rate of reachable nodes if

information diffusion start from each node

Reachability:

𝛼 =1

5

5

5+4

5+1

5+4

5+4

5

= 0.72

Reachability

• Reachability: Rate of reachable nodes if information diffusion start from each node

Low Reachability

Many nodes can not reach from start node

Difficult to diffuse information

Node Assortativity

• Correlatoin between in-degree and out-

degree

– High in-degree nodes have high degree node:

Positive Assortativity

– High in-degree nodes have low degree node:

Negative Assortativity

High node assortativity Low node assortativity

In-degree and out-degree

• High in-degree node: High ability to collect

information

• High out-degree node: High ability to diffuse

information

Both abilities are required

to diffuse information

No enough information Intercept information

High Node Assortativity

• To diffuse information:

– High information collect ability

– High information sending ability

High Node Assortativity

Low Node

Assortativity

Low Node

Assortativity

Indexes of Real Network

• How about real network?

– Real network changed to desirable structure

– Was twitter network changes to the BEST

structure for information diffusion?

• To realize more effective structure

– What was enough and what was not

– Check their network indexes

Network indexes of real network

• Before the disaster(10th Mar, 2011)

• After the disaster(12th Mar, 2011)


Node Assortativity


Power Index(Out)


Power Index(In)

0.273 0.953 2.51 -0.841 -1.94


Node Assortativity


Power Index(Out)


Power Index(In)

0.0105 0.948 0.737 -2.81 -1.12

Diffusion capability of real

network• Higher reachability after the disaster

– Before network α = 0 . 370

– After network α = 0 . 436

• Lower Node assortativity after the disaster

– Before network: r node = 0 . 273

– after network: r node = 0 . 0105

Improved

Deteriorated

Diffusion capability of real

network• To realize wider information diffusion

• Keep higher Node assortativity

High node assortativity

High in-degree node

with High out-degree

Conclusions 1/2

• The usage of Twitter per user increased after

the earthquake

• The numbers of bots decreased

• Many users with little experience with such

specific functions as reply and retweet did not

continuously use them after the disaster.

Conclusion 2/2

• Twitter networks desirable for information

diffusion?

– Change to desirable structure

• How can we realize desirable networks?

– Higher reachability and Higher Node Assortativity

Report

• Read an academic paper about disaster

and social media (facebook, twitter, and so

on)

• Report about the paper

– Information (title, journal, page, authors)

– Key method and result of the paper

– What you feel

• A4 1page

Deadline : 7th May, 2015

~how twitter used during disaster~syrinx.q.t.u-tokyo.ac.jp/tori/lecture/2015/resilience...social...

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