Nobuyuki Igata

Rumor detection on twitter

Twitter is useful in a situation of disaster for communication, announcement, request for rescue and so on. On the other hand, it causes a negative by-product, spreading rumors. This paper describe how rumors have spread after a disaster of earthquake, and discuss how can we deal with them. We first investigated actual instances of rumor after the disaster. And then we attempted to disclose characteristics of those rumors. Based on the investigation we developed a system which detects candidates of rumor from twitter and then evaluated it. The result of experiment shows the proposed algorithm can find rumors with acceptable accuracy.

Can twitter be an alternative of real-world sensors?

Twitter is the most famous on-line microblogging service now. People can post (tweet) what they are doing in 140 characters. Since Twitter posts (tweets) reflect what people are looking, hearing, feeling and so on, we can obtain information about Real-world phenomena through the large amount of tweets. In other words, Twitter can be regarded as a sensor of Real-world phenomena including natural phenomena such as hay fever. This motivated us to investigate whether can Twitter be an alternative of Real-world Sensor. In this paper, we first describe about our system which collects and analyzes tweets in order to generates a hay fever map just like as a weather report map. There are some difficulties such as location estimation and normalization of number of tweets. Using the output of the system, we discuss the comparison with actual pollen data gathered by real sensors. The result shows that Twitter can reflect natural phenomena in some particular areas.

Link scientific publications using linked data

Scientific publication management services are changing drastically. On the one hand, researchers demand intelligent search services to discover scientific publications. On the other hand, publishers need to incorporate semantic information to better organize their digital assets and make publications more discoverable. For this purpose, we investigate how to manage scientific publications using Linked Data and introduce FELinker, an entity linking component that links scientific publications with DBPedia. In particular, this paper introduces advantages of linking scientific publications with Linked Data, discusses major challenges, and outlines the proposed method. Experiment shows the proposed method could get promising performance in scientific publication linkage.