A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter
In massive Twitter datasets, tweets deriving from different domains, e.g., civil unrest, can be extracted to constitute spatio-temporal Twitter events for spatio-temporal distribution pattern detection. Existing algorithms generally employ scan statistics to detect spatio-temporal hotspots from Twit...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-10-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2220-9964/5/10/193 |
| _version_ | 1828807463054344192 |
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| author | Yan Shi Min Deng Xuexi Yang Qiliang Liu Liang Zhao Chang-Tien Lu |
| author_facet | Yan Shi Min Deng Xuexi Yang Qiliang Liu Liang Zhao Chang-Tien Lu |
| author_sort | Yan Shi |
| collection | DOAJ |
| description | In massive Twitter datasets, tweets deriving from different domains, e.g., civil unrest, can be extracted to constitute spatio-temporal Twitter events for spatio-temporal distribution pattern detection. Existing algorithms generally employ scan statistics to detect spatio-temporal hotspots from Twitter events and do not consider the spatio-temporal evolving process of Twitter events. In this paper, a framework is proposed to discover evolving domain related spatio-temporal patterns from Twitter data. Given a target domain, a dynamic query expansion is employed to extract related tweets to form spatio-temporal Twitter events. The new spatial clustering approach proposed here is based on the use of multi-level constrained Delaunay triangulation to capture the spatial distribution patterns of Twitter events. An additional spatio-temporal clustering process is then performed to reveal spatio-temporal clusters and outliers that are evolving into spatial distribution patterns. Extensive experiments on Twitter datasets related to an outbreak of civil unrest in Mexico demonstrate the effectiveness and practicability of the new method. The proposed method will be helpful to accurately predict the spatio-temporal evolution process of Twitter events, which belongs to a deeper geographical analysis of spatio-temporal Big Data. |
| first_indexed | 2024-12-12T08:26:09Z |
| format | Article |
| id | doaj.art-83102bfa31814b28ac55b2f1a1bf5c50 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-12T08:26:09Z |
| publishDate | 2016-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-83102bfa31814b28ac55b2f1a1bf5c502022-12-22T00:31:15ZengMDPI AGISPRS International Journal of Geo-Information2220-99642016-10-0151019310.3390/ijgi5100193ijgi5100193A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in TwitterYan Shi0Min Deng1Xuexi Yang2Qiliang Liu3Liang Zhao4Chang-Tien Lu5State Key Laboratory of Information Engineering in Surveying, Mapping & Remote Sensing, Wuhan University, Wuhan 430079, ChinaDepartment of Geo-informatics, Central South University, Changsha 410083, ChinaDepartment of Geo-informatics, Central South University, Changsha 410083, ChinaDepartment of Geo-informatics, Central South University, Changsha 410083, ChinaDepartment of Computer Science, Virginia Tech, Falls Church, VA 22043, USADepartment of Computer Science, Virginia Tech, Falls Church, VA 22043, USAIn massive Twitter datasets, tweets deriving from different domains, e.g., civil unrest, can be extracted to constitute spatio-temporal Twitter events for spatio-temporal distribution pattern detection. Existing algorithms generally employ scan statistics to detect spatio-temporal hotspots from Twitter events and do not consider the spatio-temporal evolving process of Twitter events. In this paper, a framework is proposed to discover evolving domain related spatio-temporal patterns from Twitter data. Given a target domain, a dynamic query expansion is employed to extract related tweets to form spatio-temporal Twitter events. The new spatial clustering approach proposed here is based on the use of multi-level constrained Delaunay triangulation to capture the spatial distribution patterns of Twitter events. An additional spatio-temporal clustering process is then performed to reveal spatio-temporal clusters and outliers that are evolving into spatial distribution patterns. Extensive experiments on Twitter datasets related to an outbreak of civil unrest in Mexico demonstrate the effectiveness and practicability of the new method. The proposed method will be helpful to accurately predict the spatio-temporal evolution process of Twitter events, which belongs to a deeper geographical analysis of spatio-temporal Big Data.http://www.mdpi.com/2220-9964/5/10/193Evolving spatio-temporal patternstarget domainsspatio-temporal Twitter eventsspatial clusteringspatio-temporal clustering |
| spellingShingle | Yan Shi Min Deng Xuexi Yang Qiliang Liu Liang Zhao Chang-Tien Lu A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter ISPRS International Journal of Geo-Information Evolving spatio-temporal patterns target domains spatio-temporal Twitter events spatial clustering spatio-temporal clustering |
| title | A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter |
| title_full | A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter |
| title_fullStr | A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter |
| title_full_unstemmed | A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter |
| title_short | A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter |
| title_sort | framework for discovering evolving domain related spatio temporal patterns in twitter |
| topic | Evolving spatio-temporal patterns target domains spatio-temporal Twitter events spatial clustering spatio-temporal clustering |
| url | http://www.mdpi.com/2220-9964/5/10/193 |
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