Semantic annotation of mobility data using social media

Fei Wu, Zhenhui Li, Wang Chien Lee, Hongjian Wang, Zhuojie Huang

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

33 Scopus citations

Abstract

Recent developments in sensors, GPS and smart phones have provided us with a large amount of mobility data. At the same time, large-scale crowd-generated social media data, such as geo-Tagged tweets, provide rich semantic information about locations and events. Combining the mobility data and surrounding social media data enables us to semantically understand why a person travels to a location at a particular time (e.g., attending a local event or visiting a point of interest). Previous research on mobility data mining has been mainly focused on mining patterns using only the mobility data. In this paper, we study the problem of using social media to annotate mobility data. As social media data is often noisy, the key research problem lies in using the right model to retrieve only the relevant words with respect to a mobility record. We propose frequency-based method, Gaussian mixture model, and kernel density estimation (KDE) to tackle this problem. We show that KDE is the most suitable model as it captures the locality of word distribution very well. We test our proposal using the real dataset collected from Twitter and demonstrate the effectiveness of our techniques via both interesting case studies and a comprehensive evaluation.

Original languageEnglish (US)
Title of host publicationWWW 2015 - Proceedings of the 24th International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Pages1253-1263
Number of pages11
ISBN (Electronic)9781450334693
DOIs
StatePublished - May 18 2015
Event24th International Conference on World Wide Web, WWW 2015 - Florence, Italy
Duration: May 18 2015May 22 2015

Publication series

NameWWW 2015 - Proceedings of the 24th International Conference on World Wide Web

Other

Other24th International Conference on World Wide Web, WWW 2015
CountryItaly
CityFlorence
Period5/18/155/22/15

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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  • Cite this

    Wu, F., Li, Z., Lee, W. C., Wang, H., & Huang, Z. (2015). Semantic annotation of mobility data using social media. In WWW 2015 - Proceedings of the 24th International Conference on World Wide Web (pp. 1253-1263). (WWW 2015 - Proceedings of the 24th International Conference on World Wide Web). Association for Computing Machinery, Inc. https://doi.org/10.1145/2736277.2741675