Information Extraction from Twitter Considering Spatial Structure
ISBN 978-85-88783-11-9
Authors
1Fujita, H.
1THE UNIVERSITY OF ELECTRO-COMMUNICATIONS Email: fujita@is.uec.ac.jp
Abstract
Mobile social media represented by Twitter is expected to be a suitable source for analyzing human behavior and status of locations. It seems that we can provide location-based information simply by spatially filtering archived data. However, there are several problems in terms of practical use. This research considers in particular problems that concern the relationship between data meaning and their spatial structures. With regard to Twitter, in general, the location from where a tweet is posted is attached to a geotagged tweet. For example, the location coordinates attached to the geotagged tweet "Heavy rain in Miura Peninsula" by NHK (Japan’s public broadcaster) are not those of the Miura Peninsula, but Shibuya in Tokyo (where NHK is located). Therefore, the tweet is not found by a spatial search around the Miura Peninsula or even Kanagawa Prefecture (where the Miura Peninsula is located). In order to resolve such problems, we propose a framework that distinguishes locations of interest and locations of activity. We propose a method for automatically classifying such locations, and developed a data collection, classification, and visualization system based on such method.