Online city-scale hyper-local event detection via analysis of social media and human mobility

Jun Hu, Yuxin Wang, Ping Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we investigate the problem of detecting real-time city-scale hyper-local events based on the analysis of social media and human mobility. Different from general events reported by news media, hyper-local events refer to both small-scale and large-scale events pertaining to a geographical location. Since small-scale events, e.g, a party in a pub, an exhibition in a local museum, are not often reported through mainstream media platforms, such as newspapers, TV news, web media or government report, it is challenging to obtain the resources related to such kind of events. Besides, those media platforms have a great latency in reporting the news of ongoing events, resulting in that the events we saw might take place a couple of days ago. Though people have tried to find clues of events from real-time social media streams (e.g., Instagram and Twitter), the scarcity of social posts with geo-tagged information leads to a very low quality of localized event detection. In this paper, in addition to the data from social media stream, we apply human mobility data which contain rich spatial-temporal information as another important resource to improve the performance of hyper-local event detection. Specifically, we use taxi data as it is expected that the occurrence of hyper-local events usually leads to the change in the surrounding traffic. As far as our knowledge, this is the first work which combines multiple social media data sources with human mobility information for the task of real-time hyper-local event detection. We propose a two-step framework which is composed of an anomaly filter and an event classifier. Through experiments on New York City data, we show that our proposed system can effectively detect both small-scale and large-scale local events. Furthermore, we verify that applying human mobility data can significantly enhance the performance of event detection and classification.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages626-635
Number of pages10
ISBN (Electronic)9781538627143
DOIs
StatePublished - Jul 1 2017
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
CountryUnited States
CityBoston
Period12/11/1712/14/17

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

Keywords

  • anomaly detection
  • human mobility
  • real-time event detection

Cite this

Hu, J., Wang, Y., & Li, P. (2017). Online city-scale hyper-local event detection via analysis of social media and human mobility. In J-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, & M. Toyoda (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (pp. 626-635). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8257978