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Localize Online Social Network User via Social Sensing

Authors:

Zidong Yang,

Shibo He,

Jiming Chen,

Youxian SunAuthors Info & Claims

SocialSens'17: Proceedings of the 2nd International Workshop on Social Sensing

Page 99

https://doi.org/10.1145/3055601.3055619

Published: 18 April 2017 Publication History

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Abstract

Dynamically localizing users in online social networks is challenging because people seldom post location-related microblogs due to privacy concern. To increase inference accuracy, a promising approach is to leverage microblogs from friends. However, it is difficult because microblogs from friends may not be synchronized or informative. To tackle these challenges, we propose a system consisting two steps. Firstly, "co-location" friends are detected and used to infer the statistical locations of users. Secondly, users' dynamic locations are determined by considering both statistical locations and POI(point of interest) names in microblogs. Experiments based on real world dataset demonstrates that our approach outperforms previous studies.

References

[1]

Bo Han, Paul Cook, and Timothy Baldwin. 2013. A Stacking-based Approach to Twitter User Geolocation Prediction. In ACL (Conference System Demonstrations). 7--12.

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[2]

Rui Li, Shengjie Wang, Hongbo Deng, Rui Wang, and Kevin Chen-Chuan Chang. 2012. Towards social user profiling: unified and discriminative influence model for inferring home locations. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1023--1031.

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SocialSens'17: Proceedings of the 2nd International Workshop on Social Sensing

April 2017

97 pages

ISBN:9781450349772

DOI:10.1145/3055601

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

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Published: 18 April 2017

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CPS Week '17

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SIGBED

CPS Week '17: Cyber Physical Systems Week 2017

April 18 - 21, 2017

PA, Pittsburgh, USA

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Investigating Homophily in Online Social Networks

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