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
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
• 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 network changes?
– Network Structure
– Diffusion Capability
• How can we realize desirable networks?
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
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
• 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 network changes?
– Network Structure
– Diffusion Capability
• How can we realize desirable networks?
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
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
• 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 network changes?
– Network Structure
– Diffusion Capability
• How can we realize desirable networks?
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?
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%
Non-PreUser 15.1% 68.1%
RetweetPreUser 51.0% 90.8%
Non-PreUser 12.5% 43.1%
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
• 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 network changes?
– Network Structure
– Diffusion Capability
• How can we realize desirable networks?
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
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
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
• 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 network changes?
– Network Structure
– Diffusion Capability
• How can we realize desirable networks?
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
Today’s Topic
• 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 network changes?
– Network Structure
– Diffusion Capability
• How can we realize desirable networks?
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
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
Reciprocity Reachability Transitibity Cyerosity Assortativity0.176 0.848 -0.0173 0.205 -0.185
Node Assortativity
Determination coefficient(Out)
Power Index(Out)
Determination coefficient(In)
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)
Reciprocity Reachability Transitibity Cyerosity Assortativity0.527 0.370 -0.0633 0.0381 -0.0998
Node Assortativity
Determination coefficient(Out)
Power Index(Out)
Determination coefficient(In)
Power Index(In)
0.273 0.953 2.51 -0.841 -1.94
Reciprocity Reachability Transitibity Cyerosity Assortativity0.232 0.436 -0.0417 0.0172 -0.221
Node Assortativity
Determination coefficient(Out)
Power Index(Out)
Determination coefficient(In)
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.
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