Sophy: a morphological framework for structuring geo-referenced social media. Kim, K., Ogawa, H., Nakamura, A., & Kojima, I. In Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks - LBSN '14, pages 31–40, Dallas/Fort Worth, Texas, 2014. ACM Press. ![Sophy: a morphological framework for structuring geo-referenced social media [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Social networks have played a crucial role of information channels for understanding our daily lives beyond communication tools. In particular, their coupling with geographic location has boosted the worth of social media to detect, track, and predicate dynamic events and situations in the real world. While the amounts of geo-tagged social media are apparently increasing at every moment, we have few framework to handle spatiotemporal changes and analyze their relationships. In this paper, we propose a framework to understand dynamic social phenomena from the mountains of fragmented, noisy data flooding social media. First, we design a data model to describe morphological features of the populations of geo-location of social media and define a set of relationships by using differential measurements in spatial, temporal, and semantic dimensions. Then, we describe 提取形态学特征ofru；ormresatrl-etaimmeingfratmweeewtso,rkcrteoateextthraecttopmoolorpghicoaml eretrlaictiofenasthuirpess, 创建拓扑关系；and store all features into a graph-based database. In the 存储在图结构 experiments, we show case studies related to two typhoons 的数据库中。 (Neoguri and Halong) and a landslide disaster (Hiroshima) with real tweet-sets in a visualization way.
@inproceedings{kim_sophy:_2014-1,
address = {Dallas/Fort Worth, Texas},
title = {Sophy: a morphological framework for structuring geo-referenced social media},
isbn = {978-1-4503-3140-1},
shorttitle = {Sophy},
url = {http://dl.acm.org/citation.cfm?doid=2755492.2755498},
doi = {10.1145/2755492.2755498},
abstract = {Social networks have played a crucial role of information channels for understanding our daily lives beyond communication tools. In particular, their coupling with geographic location has boosted the worth of social media to detect, track, and predicate dynamic events and situations in the real world. While the amounts of geo-tagged social media are apparently increasing at every moment, we have few framework to handle spatiotemporal changes and analyze their relationships. In this paper, we propose a framework to understand dynamic social phenomena from the mountains of fragmented, noisy data flooding social media. First, we design a data model to describe morphological features of the populations of geo-location of social media and define a set of relationships by using differential measurements in spatial, temporal, and semantic dimensions. Then, we describe 提取形态学特征ofru；ormresatrl-etaimmeingfratmweeewtso,rkcrteoateextthraecttopmoolorpghicoaml eretrlaictiofenasthuirpess, 创建拓扑关系；and store all features into a graph-based database. In the 存储在图结构 experiments, we show case studies related to two typhoons 的数据库中。 (Neoguri and Halong) and a landslide disaster (Hiroshima) with real tweet-sets in a visualization way.},
language = {en},
urldate = {2018-12-17},
booktitle = {Proceedings of the 7th {ACM} {SIGSPATIAL} {International} {Workshop} on {Location}-{Based} {Social} {Networks} - {LBSN} '14},
publisher = {ACM Press},
author = {Kim, Kyoung-Sook and Ogawa, Hirotaka and Nakamura, Akihito and Kojima, Isao},
year = {2014},
pages = {31--40},
}
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