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"Geo-social media analytics: exploring and exploiting geo-social experience from crowd-sourced lifelogs" by R. Lee, S. Wakamiya, and K. Sumiya with Ching-man Au Yeung as coordinator

Published: 01 April 2014 Publication History

Abstract

Geo-social media represents geo-tagged crowd-sourced media emerged from the wide-spread dissemination of smartphones and the availability of social media during daily social activities. Nowadays, with such novel media as a fertile ground to observe a variety of social phenomena, we can explore geo-social knowledge with the unprecedented scale of crowd lifelogs. In this article, we will overview our pioneering work that has been conducted to explore and exploit geo-social knowledge utilizing geo-tagged twitter data. In our study, we established a model to look into crowd behavior and mental status that are observed from local twitter data. Based on our novel perspective to examine the macro-scale crowd lifestyle patterns, we attempted to explore and exploit three types of geo-social knowledge; local event detectio, urban area characterization, and crowd sense of distance in urban space. In the conclusion, we will summarize our contribution to take advantages of the explosively growing geo-social media and briefly describe our future direction.

References

[1]
Alan Mislove, Sune Lehmann, Yong-Yeol Ahn, Jukka-Pekka Onnela, J. Niels Rosenquist: Pulse of the nation: US mood throughout the day inferred from twitter. http://www.ccs. neu.edu/home/amislove/twittermood/ (Accessed February 28, 2014)
[2]
Ryong Lee, Shoko Wakamiya, and Kazutoshi Sumiya: Discovery of Unusual Regional Social Activities using Geo-tagged Microblogs, World Wide Web Journal Special Issue on Mobile Services on the Web, Volume 14, Number 4, pp. 321-349 (2011)
[3]
Ryong Lee, Shoko Wakamiya, and Kazutoshi Sumiya: Urban Area Characterization Based on Crowd Behavioral Lifelogs over Twitter, Personal and Ubiquitous Computin, Volume 17, Issue 4, pp. 605-620 (2013)
[4]
Shoko Wakamiya, Ryong Lee, and Kazutoshi Sumiya: Social-Urban Neighborhood Search based on Crowd Footprints Network, In Proc. of the 5th International Conference on Social Informatics (SocInfo2013), Volume 8238, pp. 429-442 (2013)
[5]
Ryong Lee, Shoko Wakamiya, and Kazutoshi Sumiya: Exploring Geospatial Cognition based on Location-based Social Network Sites, World Wide Web Journal (to ap ear)

Cited By

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  • (2015)Twitter-based Urban Area Characterization by Non-negative Matrix FactorizationProceedings of the 2015 International Conference on Big Data Applications and Services10.1145/2837060.2837079(128-135)Online publication date: 20-Oct-2015
  • (2015)Measuring Crowd Mood in City Space Through TwitterWeb and Wireless Geographical Information Systems10.1007/978-3-319-18251-3_3(37-49)Online publication date: 23-Apr-2015
  • (2014)Crowd-sourced prediction of pedestrian congestion for bike navigation systemsProceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming10.1145/2676552.2676562(25-32)Online publication date: 4-Nov-2014

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  1. "Geo-social media analytics: exploring and exploiting geo-social experience from crowd-sourced lifelogs" by R. Lee, S. Wakamiya, and K. Sumiya with Ching-man Au Yeung as coordinator

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    Published In

    cover image ACM SIGWEB Newsletter
    ACM SIGWEB Newsletter  Volume 2014, Issue Spring
    Spring 2014
    26 pages
    ISSN:1931-1745
    EISSN:1931-1435
    DOI:10.1145/2591453
    Issue’s Table of Contents
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 01 April 2014
    Published in SIGWEB Volume 2014, Issue Spring

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    Cited By

    View all
    • (2015)Twitter-based Urban Area Characterization by Non-negative Matrix FactorizationProceedings of the 2015 International Conference on Big Data Applications and Services10.1145/2837060.2837079(128-135)Online publication date: 20-Oct-2015
    • (2015)Measuring Crowd Mood in City Space Through TwitterWeb and Wireless Geographical Information Systems10.1007/978-3-319-18251-3_3(37-49)Online publication date: 23-Apr-2015
    • (2014)Crowd-sourced prediction of pedestrian congestion for bike navigation systemsProceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming10.1145/2676552.2676562(25-32)Online publication date: 4-Nov-2014

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