research-article

Location-based topic evolution

Authors:

Michael R. Lyu,

Irwin KingAuthors Info & Claims

MLBS '11: Proceedings of the 1st international workshop on Mobile location-based service

Pages 89 - 98

https://doi.org/10.1145/2025876.2025894

Published: 18 September 2011 Publication History

Abstract

As the advance of mobile technologies, geographical records can be easily embedded in the data to form the location-associated documents. For example, in Twitter, the location of tweets can be identified by the GPS locations or IP addresses from smart phones. In Flickr, photos may be tagged and recorded with GPS locations. With the geographical information, it is more likely to model users' interests in different regions so as to determine the corresponding marketing strategy. Due to its potential in providing personalized and context-aware services, several pieces of work have started to explore in this area. One stream of work tries to discover users' interest topics from location-associated documents. These models work under the assumption that words close in geographical positions are likely to be clustered into the same geographical topic. However, they attain this in a static mode. That is, they do not consider the evolution of the topics. In addition, they have to specify the total number of topics for the corpus in advance. In order to utilize the geographical information and to model the change of topics, we propose a location-based topic evolution (LBTE) model to tackle the above issues. Main advantages of our model lie that it can reveal the appearance and disappearance of the topics in different regions. Moreover, topics can be automatically determined based on the location-associated documents and its total number is not restricted to a preset value. Finally, we conduct a series of experiments on both synthetic and real-world datasets to demonstrate the merits of our proposed LBTE model in capturing users' interest topics.

References

[1]

C. E. Antoniak. Mixtures of dirichlet processes with applications to bayesian nonparametric problems. Annals of Statistics, 2(6):1152--1174, 1974.

[2]

L. Backstrom, E. Sun, and C. Marlow. Find me if you can: improving geographical prediction with social and spatial proximity. In Proceedings of the 19th international conference on World wide web, WWW '10, pages 61--70, New York, NY, USA, 2010. ACM.

Digital Library

[3]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003.

Digital Library

[4]

J. Chon and H. Cha. Lifemap: A smartphone-based context provider for location-based services. IEEE Pervasive Computing, 10(2):58--67, 2011.

Digital Library

[5]

D. J. Crandall, L. Backstrom, D. Huttenlocher, and J. Kleinberg. Mapping the world's photos. In Proceedings of the 18th international conference on World wide web, WWW '09, pages 761--770, New York, NY, USA, 2009. ACM.

Digital Library

[6]

S. Dhar and U. Varshney. Challenges and business models for mobile location-based services and advertising. Commun. ACM, 54(5):121--128, 2011.

Digital Library

[7]

N. Eagle, A. Pentland, and D. Lazer. Inferring social network structure using mobile phone data. Proceedings of the National Academy of Sciences (PNAS), 106(36):15274--15278, 2009.

[8]

K. Farrahi and D. Gatica-Perez. Discovering routines from large-scale human locations using probabilistic topic models. ACM TIST, 2(1):3, 2011.

Digital Library

[9]

F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi. Trajectory pattern mining. In KDD, pages 330--339, 2007.

Digital Library

[10]

T. Hofmann. Probabilistic latent semantic analysis. In UAI, pages 289--296, 1999.

Digital Library

[11]

T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '99, pages 50--57, New York, NY, USA, 1999. ACM.

Digital Library

[12]

Y. Liu, R. Yang, and E. Wilde. Open and decentralized access across location-based services. In WWW (Companion Volume), pages 79--80, 2011.

Digital Library

[13]

E. H.-C. Lu, V. S. Tseng, and P. S. Yu. Mining cluster-based temporal mobile sequential patterns in location-based service environments. IEEE Trans. Knowl. Data Eng., 23(6):914--927, 2011.

Digital Library

[14]

Q. Mei, C. L. 0001, H. Su, and C. Zhai. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In WWW, pages 533--542, 2006.

Digital Library

[15]

M. Meila. Comparing clusterings: an axiomatic view. In ICML, pages 577--584, 2005.

Digital Library

[16]

R. M. Neal. Markov chain sampling methods for dirichlet process mixture models. Computational and Graphical Statistics, 9:249--265, 2000.

[17]

B. K. Oksendal. Stochastic Differential Equations: An Introduction with Applications. Springer, 5 edition, 2002.

[18]

V. Rao and Y. W. Teh. Spatial normalized gamma processes. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22, pages 1554--1562. 2009.

[19]

T. Rattenbury, N. Good, and M. Naaman. Towards automatic extraction of event and place semantics from flickr tags. In SIGIR, pages 103--110, 2007.

Digital Library

[20]

T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World wide web, WWW '10, pages 851--860, New York, NY, USA, 2010. ACM.

Digital Library

[21]

S. Sizov. Geofolk: latent spatial semantics in web 2.0 social media. In WSDM'10, pages 281--290, 2010.

Digital Library

[22]

S. Sizov. Geofolk: latent spatial semantics in web 2.0 social media. In WSDM, pages 281--290, 2010.

Digital Library

[23]

Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Hierarchical dirichlet processes. Journal of the American Statistical Association, 101:1566--1581, 2006.

[24]

C. Wang, J. Wang, X. Xie, and W.-Y. Ma. Mining geographic knowledge using location aware topic model. In Proceedings of the 4th ACM workshop on Geographical information retrieval, GIR '07, pages 65--70, New York, NY, USA, 2007. ACM.

Digital Library

[25]

Z. Yin, L. Cao, J. Han, C. Zhai, and T. Huang. Geographical topic discovery and comparison. In Proceedings of the 20th international conference on World wide web, WWW '11, pages 247--256, New York, NY, USA, 2011. ACM.

Digital Library

[26]

Z. Yin, L. Cao, J. Han, C. Zhai, and T. S. Huang. Geographical topic discovery and comparison. In WWW, pages 247--256, 2011.

Digital Library

[27]

C.-H. Yun and M.-S. Chen. Mining mobile sequential patterns in a mobile commerce environment. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 37(2):278--295, 2007.

Digital Library

[28]

Y. Zheng, L. Zhang, Z. Ma, X. Xie, and W.-Y. Ma. Recommending friends and locations based on individual location history. TWEB, 5(1):5, 2011.

Digital Library

Cited By

Singh JPandey DSingh A(2023)Event detection from real-time twitter streaming data using community detection algorithmMultimedia Tools and Applications10.1007/s11042-023-16263-383:8(23437-23464)Online publication date: 16-Aug-2023
https://doi.org/10.1007/s11042-023-16263-3
Mehler AGleim RGaitsch RHemati WUslu TBeckage N(2020)From Topic Networks to Distributed Cognitive MapsComplexity10.1155/2020/46070252020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/4607025
Singh JKaur ISingh A(2019)Event detection from Twitter data2019 4th International Conference on Information Systems and Computer Networks (ISCON)10.1109/ISCON47742.2019.9036286(793-798)Online publication date: Nov-2019
https://doi.org/10.1109/ISCON47742.2019.9036286
Show More Cited By

Index Terms

Location-based topic evolution
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

MLBS '11: Proceedings of the 1st international workshop on Mobile location-based service

September 2011

118 pages

ISBN:9781450309288

DOI:10.1145/2025876

Program Chairs:
S.-H. Gary Chan
The Hong Kong University of Science and Technology, Hong Kong, P.R. China
,
Edward Y. Chang
Google Research, Beijing, P.R. China
,
Michael Lyu
The Chinese University of Hong Kong, Hong Kong, P.R. China

Copyright © 2011 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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University of Florida: University of Florida
SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 September 2011

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Ubicomp '11

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Ubicomp '11: The 2011 ACM Conference on Ubiquitous Computing

September 18, 2011

Beijing, China

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Cited By

Singh JPandey DSingh A(2023)Event detection from real-time twitter streaming data using community detection algorithmMultimedia Tools and Applications10.1007/s11042-023-16263-383:8(23437-23464)Online publication date: 16-Aug-2023
https://doi.org/10.1007/s11042-023-16263-3
Mehler AGleim RGaitsch RHemati WUslu TBeckage N(2020)From Topic Networks to Distributed Cognitive MapsComplexity10.1155/2020/46070252020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/4607025
Singh JKaur ISingh A(2019)Event detection from Twitter data2019 4th International Conference on Information Systems and Computer Networks (ISCON)10.1109/ISCON47742.2019.9036286(793-798)Online publication date: Nov-2019
https://doi.org/10.1109/ISCON47742.2019.9036286
Cheng CYang HKing ILyu M(2016)A Unified Point-of-Interest Recommendation Framework in Location-Based Social NetworksACM Transactions on Intelligent Systems and Technology10.1145/29012998:1(1-21)Online publication date: 20-Sep-2016
https://dl.acm.org/doi/10.1145/2901299
Noh JLee S(2016)Extracting and evaluating topics by regionMultimedia Tools and Applications10.1007/s11042-016-3528-675:20(12765-12777)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s11042-016-3528-6
Matsui TTamura KKitakami H(2013)Location-based burst detection algorithm for georeferenced document streams based on user's moving direction2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA)10.1109/IWCIA.2013.6624784(57-62)Online publication date: Jul-2013
https://doi.org/10.1109/IWCIA.2013.6624784
Tamura KKitakami H(2013)Detecting Location-Based Enumerating Bursts in Georeferenced Micro-PostsProceedings of the 2013 Second IIAI International Conference on Advanced Applied Informatics10.1109/IIAI-AAI.2013.36(389-394)Online publication date: 31-Aug-2013
https://dl.acm.org/doi/10.1109/IIAI-AAI.2013.36
Evans CMoore PThomas A(2012)An Intelligent Mobile Advertising System (iMAS)Proceedings of the 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS)10.1109/CISIS.2012.24(959-964)Online publication date: 4-Jul-2012
https://dl.acm.org/doi/10.1109/CISIS.2012.24

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