research-article

Twitter-based Urban Area Characterization by Non-negative Matrix Factorization

Authors:

Shoko Wakamiya,

Kazutoshi SumiyaAuthors Info & Claims

BigDAS '15: Proceedings of the 2015 International Conference on Big Data Applications and Services

Pages 128 - 135

https://doi.org/10.1145/2837060.2837079

Published: 20 October 2015 Publication History

Abstract

Due to the remarkable growth of various social networks boosted by the pervasive mobile devices, massive crowds can become social sensors which can share microbolgs on a variety of social situations and natural phenomena in urban space in real-time. In order to take advantages of the novel realm of crowd-sourced lifelogs to characterize urban areas, we attempt to explore characteristics of complex and dynamic urban areas by monitoring crowd behavior via location-based social networks. In particular, we define social conditions consisting of crowd's experiential features extracted from the analysis of Twitter-based crowd's lifelogs. Then, we explore latent characteristic faces of urban areas in term of 5-dimensional social conditions by applying Non-negative Matrix Factorization (NMF). In the experiments with massive geo-tagged tweets, we classify urban areas into representative groups based on their latent patterns which enable to comprehensively understand images of the urban areas focusing on crowd's daily lives.

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Cited By

Vasquez-Henriquez PGraells-Garrido ECaro D(2020)Tweets on the Go: Gender Differences in Transport Perception and Its Discussion on Social MediaSustainability10.3390/su1213540512:13(5405)Online publication date: 3-Jul-2020
https://doi.org/10.3390/su12135405
Jin ZCao NShi YWu WWu Y(2020)EcoLens: visual analysis of ecological regions in urban contexts using traffic dataJournal of Visualization10.1007/s12650-020-00707-1Online publication date: 16-Oct-2020
https://doi.org/10.1007/s12650-020-00707-1
Liu LZhan HLiu JMan J(2019)Visual analysis of traffic data via spatio-temporal graphs and interactive topic modelingJournal of Visualization10.1007/s12650-018-0517-z22:1(141-160)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s12650-018-0517-z
Show More Cited By

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cover image ACM Other conferences

BigDAS '15: Proceedings of the 2015 International Conference on Big Data Applications and Services

October 2015

321 pages

ISBN:9781450338462

DOI:10.1145/2837060

Conference Chairs:
Jongsup Choi,
Sun Hwa Han,
Joo-Yeoun Lee,
Taeho Park,
Editor:
Aziz Nasridinov,
Program Chairs:
Carson K. Leung,
Yoo-Sung Kim,
Young-Koo Lee

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 October 2015

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BigDAS '15

BigDAS '15: 2015 International Conference on Big Data Applications and Services

October 20 - 23, 2015

Jeju Island, Republic of Korea

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Cited By

Vasquez-Henriquez PGraells-Garrido ECaro D(2020)Tweets on the Go: Gender Differences in Transport Perception and Its Discussion on Social MediaSustainability10.3390/su1213540512:13(5405)Online publication date: 3-Jul-2020
https://doi.org/10.3390/su12135405
Jin ZCao NShi YWu WWu Y(2020)EcoLens: visual analysis of ecological regions in urban contexts using traffic dataJournal of Visualization10.1007/s12650-020-00707-1Online publication date: 16-Oct-2020
https://doi.org/10.1007/s12650-020-00707-1
Liu LZhan HLiu JMan J(2019)Visual analysis of traffic data via spatio-temporal graphs and interactive topic modelingJournal of Visualization10.1007/s12650-018-0517-z22:1(141-160)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s12650-018-0517-z
Graells-Garrido ECaro DParra D(2018)Inferring modes of transportation using mobile phone dataEPJ Data Science10.1140/epjds/s13688-018-0177-17:1Online publication date: 4-Dec-2018
https://doi.org/10.1140/epjds/s13688-018-0177-1

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