research-article

How events unfold: spatiotemporal mining in social media

Authors:

Naren RamakrishnanAuthors Info & Claims

SIGSPATIAL Special, Volume 7, Issue 3

Pages 19 - 25

https://doi.org/10.1145/2876480.2876485

Published: 11 January 2016 Publication History

Abstract

There has been significant recent interest in the application of social media analytics for spatiotemporal event mining. However, no structured survey exists to capture developments in this space. This paper seeks to fill this void by reviewing recent research trends. Three branches of research are summarized here---corresponding (resp.) to modeling the past, present, and future---information tracking and backward analysis, spatiotemporal event detection, and spatiotemporal event forecasting. Each branch is illustrated with examples, challenges, and accomplishments.

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Cited By

Bok KKim ILim JYoo J(2023)Efficient graph-based event detection scheme on social mediaInformation Sciences10.1016/j.ins.2023.119415646(119415)Online publication date: Oct-2023
https://doi.org/10.1016/j.ins.2023.119415
Wen RLi S(2020)A review of the use of geosocial media data in agent-based models for studying urban systemsBig Earth Data10.1080/20964471.2020.1810492(1-19)Online publication date: 10-Sep-2020
https://doi.org/10.1080/20964471.2020.1810492
Jang GMyaeng S(2018)Predicting event mentions based on a semantic analysis of microblogs for inter-region relationshipsJournal of Information Science10.1177/016555151876101244:6(818-829)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1177/0165551518761012
Show More Cited By

Index Terms

How events unfold: spatiotemporal mining in social media
1. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image SIGSPATIAL Special

SIGSPATIAL Special Volume 7, Issue 3

November 2015

38 pages

EISSN:1946-7729

DOI:10.1145/2876480

Editor:
Chi-Yin (Ted) Chow
City University of Hong Kong

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Copyright © 2016 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 January 2016

Published in SIGSPATIAL Volume 7, Issue 3

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Cited By

Bok KKim ILim JYoo J(2023)Efficient graph-based event detection scheme on social mediaInformation Sciences10.1016/j.ins.2023.119415646(119415)Online publication date: Oct-2023
https://doi.org/10.1016/j.ins.2023.119415
Wen RLi S(2020)A review of the use of geosocial media data in agent-based models for studying urban systemsBig Earth Data10.1080/20964471.2020.1810492(1-19)Online publication date: 10-Sep-2020
https://doi.org/10.1080/20964471.2020.1810492
Jang GMyaeng S(2018)Predicting event mentions based on a semantic analysis of microblogs for inter-region relationshipsJournal of Information Science10.1177/016555151876101244:6(818-829)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1177/0165551518761012
Arcaini PBordogna G(2018)Geotemporal Querying of Social Networks and SummarizationEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_110156(920-928)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_110156
Arcaini PBordogna G(2017)Geotemporal Querying of Social Networks and SummarizationEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-7163-9_110156-1(1-10)Online publication date: 27-May-2017
https://doi.org/10.1007/978-1-4614-7163-9_110156-1
Zagal RMata FClaramunt CSarwat M(2016)Geographical Knowledge Discovery applied to the Social Perception of Pollution in the City of MexicoProceedings of the 9th ACM SIGSPATIAL Workshop on Location-based Social Networks10.1145/3021304.3021307(1-6)Online publication date: 31-Oct-2016
https://dl.acm.org/doi/10.1145/3021304.3021307

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