Lecture in UTP

Information Sharing on Social

Media under Disaster Situation-How to Designing Desirable

Networks-

Fujio Toriumi

The University of Tokyo

Short Lecture in UTP

24th Oct, 2014

Self Introduction 1/2

• Fujio Toriumi

• Affiliation

– The University of Tokyo

– Associate Professor

• Research Field

– Artificial Intelligence

• Social Data Mining

– Web & Social Media, Financial informatics

• Agent Based Modeling & Simulation

Self Introduction 2/2

• Research Theme

– Information Sharing in Disaster Situation

– Finding Risks from Social Data

• Rumors, Flaming, Information Overload, Unusual

Changes in Financial Market

– Design Social Systems by Simulation

• Information Diffusion, Game Theory, Stock Market,

Innovative Community, Community Formation,

Prediction Market

– Communication Games

• Social Game Analysis , AI for “Are you werewolf?”

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

• How can we realize desirable networks?

Research Questions &

Conclusions• Focus on Twitter under the Disaster Situation

• RQ1:How twitter changed before and after

the disaster?

– Communication tools to Information sharing tool

• RQ2:Was Twitter networks desirable for

information diffusion?

– Changed to desirable structure

• RQ3:How can we realize desirable networks?

– Higher reachability and Higher Node Assortativity

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

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

– Reply :Communication

– Retweet : Information Diffusion

– HashTag : Information clustering & searching

• 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?

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%


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

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

Today’s Topic


disaster situation?








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


– Higher reachability and Higher Node Assortativity

Data & Analytics

Lecture in UTP