Text-based geolocation prediction of social media users with neural networks

Ismini Lourentzou; Alex Morales; Chengxiang Zhai

doi:10.1109/BigData.2017.8257985

Text-based geolocation prediction of social media users with neural networks

Ismini Lourentzou, Alex Morales, Chengxiang Zhai

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Inferring the location of a user has been a valuable step for many applications that leverage social media, such as marketing, security monitoring and recommendation systems. Motivated by the recent success of Deep Learning techniques for many other tasks such as computer vision, speech recognition, and natural language processing, we study the application of neural networks to the problem of geolocation prediction and experiment with multiple techniques to improve neural networks for geolocation inference based solely on text. Experimental results on three Twitter datasets suggest that choosing appropriate network architecture, activation function, and performing Batch Normalization, can all increase performance on this task.

Original language	English (US)
Title of host publication	Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Editors	Jian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	696-705
Number of pages	10
ISBN (Electronic)	9781538627143
DOIs	https://doi.org/10.1109/BigData.2017.8257985
State	Published - Jul 1 2017
Event	5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States Duration: Dec 11 2017 → Dec 14 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume	2018-January

Other

Other	5th IEEE International Conference on Big Data, Big Data 2017
Country/Territory	United States
City	Boston
Period	12/11/17 → 12/14/17

Keywords

Deep Learning
Geolocation prediction
Neural Networks
Text-based Geotagging

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Information Systems
Information Systems and Management
Control and Optimization

Online availability

10.1109/BigData.2017.8257985

Library availability

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Cite this

Lourentzou, I., Morales, A., & Zhai, C. (2017). Text-based geolocation prediction of social media users with neural networks. In J-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, & M. Toyoda (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (pp. 696-705). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8257985

Text-based geolocation prediction of social media users with neural networks. / Lourentzou, Ismini; Morales, Alex; Zhai, Chengxiang.

Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. ed. / Jian-Yun Nie; Zoran Obradovic; Toyotaro Suzumura; Rumi Ghosh; Raghunath Nambiar; Chonggang Wang; Hui Zang; Ricardo Baeza-Yates; Ricardo Baeza-Yates; Xiaohua Hu; Jeremy Kepner; Alfredo Cuzzocrea; Jian Tang; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. p. 696-705 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lourentzou, I, Morales, A & Zhai, C 2017, Text-based geolocation prediction of social media users with neural networks. in J-Y Nie, Z Obradovic, T Suzumura, R Ghosh, R Nambiar, C Wang, H Zang, R Baeza-Yates, R Baeza-Yates, X Hu, J Kepner, A Cuzzocrea, J Tang & M Toyoda (eds), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 696-705, 5th IEEE International Conference on Big Data, Big Data 2017, Boston, United States, 12/11/17. https://doi.org/10.1109/BigData.2017.8257985

Lourentzou I, Morales A, Zhai C. Text-based geolocation prediction of social media users with neural networks. In Nie J-Y, Obradovic Z, Suzumura T, Ghosh R, Nambiar R, Wang C, Zang H, Baeza-Yates R, Baeza-Yates R, Hu X, Kepner J, Cuzzocrea A, Tang J, Toyoda M, editors, Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 696-705. (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017). doi: 10.1109/BigData.2017.8257985

Lourentzou, Ismini ; Morales, Alex ; Zhai, Chengxiang. / Text-based geolocation prediction of social media users with neural networks. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. editor / Jian-Yun Nie ; Zoran Obradovic ; Toyotaro Suzumura ; Rumi Ghosh ; Raghunath Nambiar ; Chonggang Wang ; Hui Zang ; Ricardo Baeza-Yates ; Ricardo Baeza-Yates ; Xiaohua Hu ; Jeremy Kepner ; Alfredo Cuzzocrea ; Jian Tang ; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 696-705 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017).

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title = "Text-based geolocation prediction of social media users with neural networks",

abstract = "Inferring the location of a user has been a valuable step for many applications that leverage social media, such as marketing, security monitoring and recommendation systems. Motivated by the recent success of Deep Learning techniques for many other tasks such as computer vision, speech recognition, and natural language processing, we study the application of neural networks to the problem of geolocation prediction and experiment with multiple techniques to improve neural networks for geolocation inference based solely on text. Experimental results on three Twitter datasets suggest that choosing appropriate network architecture, activation function, and performing Batch Normalization, can all increase performance on this task.",

keywords = "Deep Learning, Geolocation prediction, Neural Networks, Text-based Geotagging",

author = "Ismini Lourentzou and Alex Morales and Chengxiang Zhai",

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N2 - Inferring the location of a user has been a valuable step for many applications that leverage social media, such as marketing, security monitoring and recommendation systems. Motivated by the recent success of Deep Learning techniques for many other tasks such as computer vision, speech recognition, and natural language processing, we study the application of neural networks to the problem of geolocation prediction and experiment with multiple techniques to improve neural networks for geolocation inference based solely on text. Experimental results on three Twitter datasets suggest that choosing appropriate network architecture, activation function, and performing Batch Normalization, can all increase performance on this task.

AB - Inferring the location of a user has been a valuable step for many applications that leverage social media, such as marketing, security monitoring and recommendation systems. Motivated by the recent success of Deep Learning techniques for many other tasks such as computer vision, speech recognition, and natural language processing, we study the application of neural networks to the problem of geolocation prediction and experiment with multiple techniques to improve neural networks for geolocation inference based solely on text. Experimental results on three Twitter datasets suggest that choosing appropriate network architecture, activation function, and performing Batch Normalization, can all increase performance on this task.

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ER -

Text-based geolocation prediction of social media users with neural networks

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Keywords

ASJC Scopus subject areas

Online availability

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