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