Semantic Twitter Analyzing Tweets For Real Time Event Notification

Semantic Twitter:Analyzing Tweets for Real-time Event Notification
Makoto Okazaki and Yutaka Matsuo
The University of Tokyo

Twitter
Popular microblogging service
Short message within 140 characters
Real-time nature

Studies on Twitter
Why we twitter: Understanding microblogging usage and communities(Java et al. 2007)
Analysis indicators for communities on microblogging platforms(Grosseck et al. 2009)
Microblogging for language learning(Borau et al. 2009)
Microblogging: A semantic and distributed approach(Passant et al. 2008)

Work on Semantic Web
How to integrate linked data on the web
Automatic extraction of semantic data
Extracting relation among entities from web pages
Extracting events

Idea
Means of integrating semantic processing and the real-time nature of Twitter have not been well studied
Combining these two directions, we can make various algorithms to process twitter data semantically

Proposal
Tweet delivery system
Delivering some tweets if they are semantically relevant to users’ information need
Example: earthquake, rainbow, traffic jam
Earthquake prediction system targeting on Japanese tweets

The concept of system
Useful information
Un-useful information
Mass media
Semantic
technology
Information
User
Real-timeliness: low
Real-timeliness: high
Real-timeliness: high
Usefulness: high
Usefulness: low
Usefulness: high
Mass media
Advanced social medium
Social media

Earthquake information
Lots of earthquakes in Japan.
Earthquake information is much more valuable if given in real time.
Japanese government has allocated a considerable amount of its budget.
Gathering information about earthquakes from twitter.

Earthquake information system
Our System
tweet
E-mail
shook!
Distance from the earthquake center
Earthquake center

System architecture
Twitter search API
Queries
Tweets
“Earthquake”
“Shakes”
Our system
Fetcher
Text Analyzer
DB
Mecab
SVM
Detect tweets about the target event
Sender
E-mail
User
User
…
User
User

Classification
Clarifying that tweet is really referring to an actual earthquake occurring
Classifier using support vector machine(SVM)
Preparing 597 examples as a training set

Features
Group A: simple statistical features
The number of words in a tweet, and the position of the query word in a tweet
Group B: keyword features
The words in a tweet.
The number of each words in a tweet.
Group C: context word features
The words before and after the query word

Performance of classification

Group A: simple statistical features
the number of words in a tweet, and the position of the query word in a tweet
Group B: keyword features
the words in a tweet
Group C: context word features
he words before and after the query word

System architecture
Twitter search API
Queries
Tweets
“Earthquake”
“Shakes”
Our system
Fetcher
Text Analyzer
DB
Mecab
SVM
Detect tweets about the target event
Sender
E-mail
User
User
…
User
User

Registration
The detection of the past earthquakes

Facts about earthquake detection

The number of tweets on earthquakes

E-mail
The location is obtained by a registered location on the user profile on twitter.
Dear Alice,
We have just detected an earthquake
around Chiba. Please take care.
Best,
Toretter Alert System

Another prototype
Rainbow information
Using a similar approach used for detecting earthquakes.
Not so time-sensitive
Rainbows can be found in various regions simultaneously
World rainbow map
No agency is reporting rainbow information

Another plan
Reporting sighting of celebrities
Map of celebrities found in cities
We specifically examine the potential uses of the technology. Of course, we should be careful about privacy issues

Related works
Tweettronics
Analysis of tweets about brands and products for marketing purposes
Web2express Digest
Auto-discovering information from twitter streaming data to find real-time interesting conversations

Conclusion
Earthquake prediction system
The system might be designated as semantic twitter
Twitter enable us to develop an advanced social medium

Thank you