This document proposes a system to analyze tweets semantically in real-time to detect and notify users of events. It describes using Twitter to detect earthquakes in Japan by classifying tweets about earthquakes vs other topics using machine learning. The system would send email alerts to registered users about detected earthquakes near their location. It also discusses expanding the system to detect rainbows or celebrity sightings from tweets. The goal is to leverage Twitter's real-time nature and semantic analysis to provide timely notifications about events from social media data.

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

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

3. 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)

4. 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

5. 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

6. 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

7. 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

8. 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.

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

10. 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

11. 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

12. 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

14. the number of words in a tweet, and the position of the query word in a tweet

15. Group B: keyword features

16. the words in a tweet

17. Group C: context word features

19. Registration The detection of the past earthquakes

20. Facts about earthquake detection

21. The number of tweets on earthquakes

22. 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

23. 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

24. 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

25. 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

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

27. Thank you