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.

References

[1]

Marta Arias, Argimiro Arratia, and Ramon Xuriguera. Forecasting with twitter data. In ACM Transactions on Intelligent Systems and Technology (TIST), volume 5, page 8. ACM, 2013.

Digital Library

[2]

David M Blei and John D Lafferty. Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning, pages 113--120. ACM, 2006.

Digital Library

[3]

Johan Bollen, Huina Mao, and Xiaojun Zeng. Twitter mood predicts the stock market. In Booktitle of Computational Science, volume 2, pages 1--8. Elsevier, 2011.

[4]

Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. Information credibility on twitter. In Proceedings of the 20th international conference on World wide web, pages 675--684. ACM, 2011.

Digital Library

[5]

Gabriel Pui Cheong Fung, Jeffrey Xu Yu, Philip S Yu, and Hongjun Lu. Parameter free bursty events detection in text streams. In Proceedings of the 31st international conference on Very large data bases, pages 181--192. VLDB Endowment, 2005.

Digital Library

[6]

Aditi Gupta, Ponnurangam Kumaraguru, Carlos Castillo, and Patrick Meier. Tweetcred: Real-time credibility assessment of content on twitter. In Social Informatics, pages 228--243. Springer, 2014.

[7]

Jingrui He, Wei Shen, Phani Divakaruni, Laura Wynter, and Rick Lawrence. Improving traffic prediction with tweet semantics. In Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, pages 1387--1393. AAAI Press, 2013.

Digital Library

[8]

Ting Hua, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. Sted: semi-supervised targeted-interest event detectionin in twitter. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1466--1469. ACM, 2013.

Digital Library

[9]

Ting Hua, Chang-Tien Lu, Naren Ramakrishnan, Feng Chen, Jaime Arredondo, David Mares, and Kristen Summers. Analyzing civil unrest through social media. In Computer, number 12, pages 80--84. IEEE, 2013.

Digital Library

[10]

Ting Hua, Ning Yue, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Topical analysis of interactions between news and social media. In Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016.

[11]

Giridhar Kumaran and James Allan. Text classification and named entities for new event detection. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pages 297--304. ACM, 2004.

Digital Library

[12]

Jure Leskovec, Lars Backstrom, and Jon Kleinberg. Meme-tracking and the dynamics of the news cycle. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 497--506. ACM, 2009.

Digital Library

[13]

Rui Li, Kin Hou Lei, Ravi Khadiwala, and Kevin Chen-Chuan Chang. Tedas: A twitter-based event detection and analysis system. In Proceedings of the 28th International Conference on, pages 1273--1276. IEEE, 2012.

Digital Library

[14]

Cindy Xide Lin, Qiaozhu Mei, Jiawei Han, Yunliang Jiang, and Marina Danilevsky. The joint inference of topic diffusion and evolution in social communities. In Data Mining (ICDM), 2011 IEEE 11th International Conference on, pages 378--387. IEEE, 2011.

Digital Library

[15]

Cindy Xide Lin, Bo Zhao, Qiaozhu Mei, and Jiawei Han. Pet: a statistical model for popular events tracking in social communities. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 929--938. ACM, 2010.

Digital Library

[16]

Jimmy Lin, Rion Snow, and William Morgan. Smoothing techniques for adaptive online language models: topic tracking in tweet streams. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 422--429. ACM, 2011.

Digital Library

[17]

Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web, pages 851--860. ACM, 2010.

Digital Library

[18]

Zeynep Tufekci and Christopher Wilson. Social media and the decision to participate in political protest: Observations from tahrir square. In Booktitle of Communication, volume 62, pages 363--379. Wiley Online Library, 2012.

[19]

Andranik Tumasjan, Timm Oliver Sprenger, Philipp G Sandner, and Isabell M Welpe. Predicting elections with twitter: What 140 characters reveal about political sentiment. In ICWSM, volume 10, pages 178--185, 2010.

[20]

Xiaofeng Wang, Matthew S Gerber, and Donald E Brown. Automatic crime prediction using events extracted from twitter posts. In Social Computing, Behavioral-Cultural Modeling and Prediction, pages 231--238. Springer, 2012.

Digital Library

[21]

Xuerui Wang and Andrew McCallum. Topics over time: a non-markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 424--433. ACM, 2006.

Digital Library

[22]

Wouter Weerkamp and Maarten de Rijke. Credibility-inspired ranking for blog post retrieval. In Information Retrieval, volume 15, pages 243--277. Springer, 2012.

Digital Library

[23]

Xintian Yang, Amol Ghoting, Yiye Ruan, and Srinivasan Parthasarathy. A framework for summarizing and analyzing twitter feeds. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 370--378. ACM, 2012.

Digital Library

[24]

Liang Zhao, Feng Chen, Jing Dai, Ting Hua, Chang-Tien Lu, and Naren Ramakrishnan. Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling. In PLOS ONE, volume 9, 2014.

[25]

Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Dynamic theme tracking in twitter. In Proceedings of the 3rd IEEE International Conference on Big Data, pages 561--570, 2015.

Digital Library

[26]

Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Spatiotemporal event forecasting in social media. In Proceedings of the 15th SIAM International Conference on Data Mining, pages 963--971, 2015.

[27]

Liang Zhao, Ting Hua, Chang-Tien Lu, and Ray Chen. A topic-focused trust model for twitter. In Computer Communications. Elsevier, 2015.

[28]

Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Multi-task learning for spatio-temporal event forecasting. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1503--1512, 2015.

Digital Library

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

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

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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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