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Towards Diversified Local Users Identification Using Location Based Social Networks

Authors:

Shenglong ZhuAuthors Info & Claims

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

Pages 115 - 118

https://doi.org/10.1145/3110025.3110159

Published: 31 July 2017 Publication History

Abstract

Identifying a set of diversified users who are local residents in a city is an important task for a wide spectrum of applications such as target ads of local business, surveys and interviews, and personalized recommendations. While many previous studies have investigated the problem of identifying the local users in a given area using online social network information (e.g., geotagged posts), few methods have been developed to solve the diversified user identification problem. In this paper, we propose a new analytical framework, Diversified Local Users Finder (DLUF), to accurately identify a set of diversified local users using a principled approach. In particular, the DLUF scheme first defines a new distance metric that measures the diversity between local users from physical dimension. The DLUF scheme then provides a solution to find the set of local users with maximum diversity. The performance of DLUF scheme is compared to several representative baselines using two real world datasets obtained from Foursquare application. We observe that the DLUF scheme accurately identifies the local users with a great diversity and significantly outperforms the compared baselines.

References

[1]

A. Ahmed, Y. Low, M. Aly, V. Josifovski, and A. J. Smola. Scalable distributed inference of dynamic user interests for behavioral targeting. In Proceedings of International Conference on Knowledge Discovery and Data Mining (KDD), pages 114--122. ACM, 2011.

Digital Library

[2]

L. Backstrom, E. Sun, and C. Marlow. Find me if you can: improving geographical prediction with social and spatial proximity. In Proceedings of International Conference on World Wide Web (WWW), pages 61--70. ACM, 2010.

Digital Library

[3]

E. Cho, S. A. Myers, and J. Leskovec. Friendship and mobility: user movement in location-based social networks. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD), pages 1082--1090. ACM, 2011.

Digital Library

[4]

R. S. Garfinkel and G. L. Nemhauser. Integer programming, volume 4. Wiley New York, 1972.

[5]

X. Geng, H. Zhang, Z. Song, Y. Yang, H. Luan, and T.-S. Chua. One of a kind: User profiling by social curation. In Proceedings of International Conference on Multimedia (MM), pages 567--576. ACM, 2014.

Digital Library

[6]

M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi. Understanding individual human mobility patterns. Nature, pages 779--782, 2008.

[7]

C. Huang and D. Wang. Exploiting spatial-temporal-social constraints for localness inference using online social media. In Proceedings of International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 287--294. IEEE, 2016.

[8]

C. Huang, D. Wang, S. Zhu, and B. Mann. Toward local family relationship discovery in location-based social network. Social Network Analysis and Mining (SNAM), 2017.

[9]

C. Huang, D. Wang, S. Zhu, and D. Y. Zhang. Towards unsupervised home location inference from online social media. In Proceedings of International Conference on Big Data (Big Data), pages 676--685. IEEE, 2016.

[10]

K. Ikeda, G. Hattori, C. Ono, H. Asoh, and T. Higashino. Twitter user profiling based on text and community mining for market analysis. Knowledge-Based Systems (KBS), 51:35--47, 2013.

Digital Library

[11]

J. Jiang, H. Lu, B. Yang, and B. Cui. Finding top-k local users in geotagged social media data. In Proceedings of International Conference on Data Engineering (ICDE), pages 267--278. IEEE, 2015.

[12]

R. Li, C. Wang, and K. C.-C. Chang. User profiling in an ego network: co-profiling attributes and relationships. In Proceedings of International Conference on World Wide Web (WWW), pages 819--830. ACM, 2014.

Digital Library

[13]

J. Mahmud, J. Nichols, and C. Drews. Where is this tweet from? inferring home locations of twitter users. International Conference on Web and Social Media (ICWSM), 12:511--514, 2012.

[14]

A. Makhorin. Glpk (gnu linear programming kit), 2008.

[15]

J. McGee, J. Caverlee, and Z. Cheng. Location prediction in social media based on tie strength. In Proceedings of International Conference on Information and Knowledge Management (CIKM), pages 459--468. ACM, 2013.

Digital Library

[16]

A. Mislove, B. Viswanath, K. P. Gummadi, and P. Druschel. You are who you know: inferring user profiles in online social networks. In Proceedings of International Conference on Web Search and Data Mining (WSDM), pages 251--260. ACM, 2010.

Digital Library

[17]

S. Scellato, A. Noulas, and C. Mascolo. Exploiting place features in link prediction on location-based social networks. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD), pages 1046--1054. ACM, 2011.

Digital Library

[18]

D. Wang, M. T. Al Amin, T. Abdelzaher, D. Roth, C. R. Voss, L. M. Kaplan, S. Tratz, J. Laoudi, and D. Briesch. Provenance-assisted classification in social networks. IEEE Journal of Selected Topics in Signal Processing, 8(4):624--637, 2014.

[19]

D. Wang and C. Huang. Confidence-aware truth estimation in social sensing applications. In Proceedings of International Conference on Sensing, Communication, and Networking (SECON), pages 336--344. IEEE, 2015.

[20]

Y. Wang, C. Cong, G. Song, and K. Xie. Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In Proceedings of International Conference on Knowledge Discovery and Data Mining (KDD), pages 1039--1048. ACM, 2010.

Digital Library

[21]

M. Ye, P. Yin, W.-C. Lee, and D.-L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the International Conference on Research and Development in Information Retrieval (SIGIR), pages 325--334. ACM, 2011.

Digital Library

[22]

D. Y. Zhang, R. Han, D. Wang, and C. Huang. On robust truth discovery in sparse social media sensing. In International Conference on Big Data (Big Data), pages 1076--1081. IEEE, 2016.

Cited By

Huang CWang DTao JMann B(2020)On Physical-Social-Aware Localness Inference by Exploring Big Data from Location-Based ServicesIEEE Transactions on Big Data10.1109/TBDATA.2017.27265516:4(679-690)Online publication date: 1-Dec-2020
https://doi.org/10.1109/TBDATA.2017.2726551
Yu KXu PSong SFeng HQiu SLiu H(2019)Location Traceability of Social Media Analysis in Urban ComplexProceedings of the 2019 2nd International Conference on Data Science and Information Technology10.1145/3352411.3352444(210-218)Online publication date: 19-Jul-2019
https://dl.acm.org/doi/10.1145/3352411.3352444
Cai CLi LZeng DMa H(2019)Exploring Writing Pattern with Pop Culture Ingredients for Social User Modeling2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852187(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852187
Show More Cited By

Towards Diversified Local Users Identification Using Location Based Social Networks
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

July 2017

698 pages

ISBN:9781450349932

DOI:10.1145/3110025

Copyright © 2017 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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Published: 31 July 2017

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ASONAM '17: Advances in Social Networks Analysis and Mining 2017

July 31 - August 3, 2017

Sydney, Australia

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Cited By

Huang CWang DTao JMann B(2020)On Physical-Social-Aware Localness Inference by Exploring Big Data from Location-Based ServicesIEEE Transactions on Big Data10.1109/TBDATA.2017.27265516:4(679-690)Online publication date: 1-Dec-2020
https://doi.org/10.1109/TBDATA.2017.2726551
Yu KXu PSong SFeng HQiu SLiu H(2019)Location Traceability of Social Media Analysis in Urban ComplexProceedings of the 2019 2nd International Conference on Data Science and Information Technology10.1145/3352411.3352444(210-218)Online publication date: 19-Jul-2019
https://dl.acm.org/doi/10.1145/3352411.3352444
Cai CLi LZeng DMa H(2019)Exploring Writing Pattern with Pop Culture Ingredients for Social User Modeling2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852187(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852187
Cai ZJiang BLu ZLiu JMa P(2019)isAnon: Flow-Based Anonymity Network Traffic Identification Using Extreme Gradient Boosting2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851964(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8851964

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