Information Extraction and Visualization from Twitter Considering Spatial Structure

Author:

Fujita Hideyuki1

Affiliation:

1. Graduate School of Informatics and Engineering / The University of Electro-communications / Chofu / Tokyo / Japan

Abstract

Mobile social media represented by Twitter are expected to be a suitable source of data for analyzing human behaviour and statuses 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 which 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 of 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). 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 develop a data collection, classification, and visualization system based on this method.

Publisher

University of Toronto Press Inc. (UTPress)

Subject

Earth-Surface Processes

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3