2日目の午前中はチュートリアルに出たけど，面白くなかったので，パス．
午後は，自分が発表するSWDM'13に参加．
SWDM'13 Workshop
Keynote
Disasters Response Using Social Life Networks
Ramesh Jain
Check this paper
Insight => Information
Social Web and Maslow's Hierarchy:
Fundamental Problem
- Connecting people to Resources
- Effectively, efficiently, and promptly in given situations
Social Life Networks = Connecting people to resources
Aggregation and composition
Disaster Management Cycle
relief response recovery rebuilding prevention mitigation preparedness
Understanding needs and availability of resources is critical
Information is the key
- Data integration
- location analysis
- and so on
Situation mapping
Resource Management
Communication and Reporting
* Micro level data, and Macro level data
Social Structures:
Person - Organizations - Human Society
From micro => macro
Data Ingestion
Actionable Insights and Recommendations
Stream Processing engnie
bridge high level concept of situation and low level data stream
E-mage data representation
spatio-temporal element
Eventshop : Motivation
- Billions of data sources
- Environment for selecting and Combining
- appropriate sources to detect situation
- predict pro-active actions
- Interactions with different types of Uesrs
Decision makers Intidviduals
# そんなシステムを作る
# dataからSituationを予測するのかな。これはやりたいことにも通じるものかも
Situation Recognition Algebra
What we have are based on our current experience.
Will be enriched.
# For Disaster Managementという観点は面白い
Social life network for Disaster Management
SOcial web has been useful in
- dissminating information
- situation awareness
- people finding
- warnings
All users are not equal
- How can we make personalized alerts?
- Turning Disassociated Data into Meaningful Information
LIMITATINOS
- Focus on being broad platform
- Informatoin must be extracted from limited text
- Difficult to extract Signal
- LOW Signal-to-Noise ratio
Tweeting applications ustbe solution
- develop focused micro blogs
- Get all information from 'motivated' and collaborative user
- Help them solve their problem
LifeSaver App
- Preditive analystics operators in eventshop
- interactions with data warehouses databses information systems
- building personal context in the form of personas and using it
- making eventshop available as open source
- developing livesaver app
# Persona 作成は面白そう。場所推定を含めて、RTの関係とかから予測できないかしら
Eventshopというアプリケーションを中心に色々やっているようで，面白そう．
講演の後話をしたので，メールでコンタクトを取ってみよう．
A Sensitive Twitter Earthquake Detector
Bella Robinson, Robert Power and Mark Cameron
Identifying earthquakes via global network of seismic stations
ESA Platform
ESA Burst Detection
Geo-location of Tweets is not easy:1% geo tags
Use Yahoo's Geoplanet service to detect user's location: 70%
特に目新しい感じはしない
Delay: 3:03 (max 5:34)
別に，ソーシャルでやる必要はないんじゃないかと．
Text vs. Images: On the Viability of Social Media to Assess Earthquake Damage
Yuan Liang, James Caverlee and John Mander
ソーシャルメディアデータからダメージは予測可能か？
TextとImageのどちらがダメージの予測に適してるのか
35%のツイートがURLを含む
Using Density
Tweet density
Retweet density
Number of user tweet
Spread Speed
Text spread constant
Media spread slow and Chaotic
Intensity Attenuation
Log RT densityは距離によってLogで減衰
減衰の様子はなかなか面白い．
Comparing Web Feeds and Tweets for Emergency Management
Robert Power, Bella Robinson and Catherine Wise.
Australian again
Emergency Response Intelligence Capability : ERIC
What information is there on Twitter about an event
Where is...
Compare Official data and Tweet data
Emergency service agencies provides userful information in different ways
Tweet from official sources:
- are reported sooner
- contains specific information
- detailed
- updated frequency
- include information from public
compare to personal tweets
Practical Extraction of Disaster-Relevant Information from Social Media
Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Diaz and Patrick Meier.
Categorised Sandy tweets
- personal
- informative
- caution and advise
- casualties and damage
- donations
Extract information that contribute to situational awareness
Filtering, Clustering, Extracting
Filtering
- Is disaster related?
- Contributes to situational awareness?
class:
- personal (also for friends)
- informative (for many people)
- others
Filter personal and others
Using WEKA
Classification
- Caution & advise
- Casualties & Damage
- Donations
- people
- information sources
- others
Using WEKA
Extraction
- Using crowdsourcing
Automatic Extractor
- Using CMU ARK Twitter NLP
- UsingCRF
70-80%位の精度で成功
Many messages contribute to situational awareness
IE can speed up management
Various informative classes
We can use Machine Learning for all 3 tasks
Location-Based Insights from the Social Web
Yohei Ikawa, Maja Vukovic, Jakob Rogstadius and Akiko Murakami
Why twitter for disaster management?
APIs and so on
Inferring Location from the Social Web
Location Types and Use Cases in Disaster Management
- Location in Text
- Focused Locatoins
- User's current Location
- User's Location Profile
Use location data in crisis tracker
Confidence Score is calculated based on
- Location Popularity
- Region Context
Confidence score is
- Location popularity x Region Context
Information Verification during Natural Disasters
Abdulfatai Popoola, Dmytro Krasnoshtan, Attila Toth, Victor Naroditskiy, Carlos Castillo, Patrick Meier and Iyad Rahwan
Boston Malathon
Social Media vs Mass media
social media で誤報．
SocialMediaは信頼に値するのか？
BBC's user-generated content hub
"Social Media news agency"
Verificatoin of authenticity of photo and video evidence
Time of day, weather, landmarks...
Accents spoken, ambient noises, etc
Truth in social media is a widespread concern
Jounarists verify, verify, verify
Computer Scientist apply ML, IR, NLP
というの言いっぷりが面白かった．
相変わらずメモになっていないメモだけど，あとで論文読めば，まあいいか．
昨日のWSよりは遙かに面白かった．
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