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

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Twitter<br />Popular microblogging service<br />Short message within 140 characters<br />Real-time nature<br />

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

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

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

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

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

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

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

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

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

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

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Performance of classification<br /><ul><li>Group A: simple statistical features

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

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Group B: keyword features

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the words in a tweet

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Group C: context word features

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he words before and after the query word</li></li></ul><li>System architecture<br />Twitter search API<br />Queries<br />Tweets<br />“Earthquake”<br />“Shakes”<br />Our system<br />Fetcher<br />Text Analyzer<br />DB<br />Mecab<br />SVM<br />Detect tweets about the target event<br />Sender<br />E-mail<br />User<br />User<br />…<br />User<br />User<br />

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Registration<br />The detection of the past earthquakes<br />

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Facts about earthquake detection<br />

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The number of tweets on earthquakes<br />

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

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

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

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

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

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Thank you<br />