Predicting various types of user attributes in Twitter by using personalized pagerank

Kazuya Uesato; Hiroki Asai; Hayato Yamana

doi:10.1109/BigData.2015.7364090

Predicting various types of user attributes in Twitter by using personalized pagerank

Kazuya Uesato, Hiroki Asai, Hayato Yamana

School of Fundamental Science and Engineering

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

1 Citation (Scopus)

Abstract

Predicting various types of user-attributes in social networks has become indispensable for personalizing applications since there are many non-disclosed attributes in social networks. However, extracted attributes in existing works are limited to pre-defined types of attributes, which results in no extraction of unexpected-types of attributes. In this paper, we therefore propose a novel method that extracts various, i.e., unlimited, types of attributes by adopting personalized PageRank to a large social network. The experimental results using over 7.9 million of Japanese Twitter-users show that our proposed method successfully extracts four types of attributes per-user in average with 0.841 of MAP@20.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
Editors	Feng Luo, Kemafor Ogan, Mohammed J. Zaki, Laura Haas, Beng Chin Ooi, Vipin Kumar, Sudarsan Rachuri, Saumyadipta Pyne, Howard Ho, Xiaohua Hu, Shipeng Yu, Morris Hui-I Hsiao, Jian Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2825-2827
Number of pages	3
ISBN (Electronic)	9781479999255
DOIs	https://doi.org/10.1109/BigData.2015.7364090
Publication status	Published - 2015 Dec 22
Event	3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States Duration: 2015 Oct 29 → 2015 Nov 1

Publication series

Name	Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

Other

Other	3rd IEEE International Conference on Big Data, IEEE Big Data 2015
Country/Territory	United States
City	Santa Clara
Period	15/10/29 → 15/11/1

Keywords

Twitter
personalized PageRank
social networking service
user attribute prediction
user profiling

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Information Systems
Software

Access to Document

10.1109/BigData.2015.7364090

Cite this

Uesato, K., Asai, H., & Yamana, H. (2015). Predicting various types of user attributes in Twitter by using personalized pagerank. In F. Luo, K. Ogan, M. J. Zaki, L. Haas, B. C. Ooi, V. Kumar, S. Rachuri, S. Pyne, H. Ho, X. Hu, S. Yu, M. H-I. Hsiao, & J. Li (Eds.), Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 (pp. 2825-2827). [7364090] (Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2015.7364090

Predicting various types of user attributes in Twitter by using personalized pagerank. / Uesato, Kazuya; Asai, Hiroki; Yamana, Hayato.

Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. ed. / Feng Luo; Kemafor Ogan; Mohammed J. Zaki; Laura Haas; Beng Chin Ooi; Vipin Kumar; Sudarsan Rachuri; Saumyadipta Pyne; Howard Ho; Xiaohua Hu; Shipeng Yu; Morris Hui-I Hsiao; Jian Li. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2825-2827 7364090 (Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015).

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

Uesato, K, Asai, H & Yamana, H 2015, Predicting various types of user attributes in Twitter by using personalized pagerank. in F Luo, K Ogan, MJ Zaki, L Haas, BC Ooi, V Kumar, S Rachuri, S Pyne, H Ho, X Hu, S Yu, MH-I Hsiao & J Li (eds), Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015., 7364090, Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015, Institute of Electrical and Electronics Engineers Inc., pp. 2825-2827, 3rd IEEE International Conference on Big Data, IEEE Big Data 2015, Santa Clara, United States, 15/10/29. https://doi.org/10.1109/BigData.2015.7364090

Uesato K, Asai H, Yamana H. Predicting various types of user attributes in Twitter by using personalized pagerank. In Luo F, Ogan K, Zaki MJ, Haas L, Ooi BC, Kumar V, Rachuri S, Pyne S, Ho H, Hu X, Yu S, Hsiao MH-I, Li J, editors, Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 2825-2827. 7364090. (Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015). doi: 10.1109/BigData.2015.7364090

Uesato, Kazuya ; Asai, Hiroki ; Yamana, Hayato. / Predicting various types of user attributes in Twitter by using personalized pagerank. Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. editor / Feng Luo ; Kemafor Ogan ; Mohammed J. Zaki ; Laura Haas ; Beng Chin Ooi ; Vipin Kumar ; Sudarsan Rachuri ; Saumyadipta Pyne ; Howard Ho ; Xiaohua Hu ; Shipeng Yu ; Morris Hui-I Hsiao ; Jian Li. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 2825-2827 (Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015).

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title = "Predicting various types of user attributes in Twitter by using personalized pagerank",

abstract = "Predicting various types of user-attributes in social networks has become indispensable for personalizing applications since there are many non-disclosed attributes in social networks. However, extracted attributes in existing works are limited to pre-defined types of attributes, which results in no extraction of unexpected-types of attributes. In this paper, we therefore propose a novel method that extracts various, i.e., unlimited, types of attributes by adopting personalized PageRank to a large social network. The experimental results using over 7.9 million of Japanese Twitter-users show that our proposed method successfully extracts four types of attributes per-user in average with 0.841 of MAP@20.",

keywords = "Twitter, personalized PageRank, social networking service, user attribute prediction, user profiling",

author = "Kazuya Uesato and Hiroki Asai and Hayato Yamana",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.; 3rd IEEE International Conference on Big Data, IEEE Big Data 2015 ; Conference date: 29-10-2015 Through 01-11-2015",

year = "2015",

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doi = "10.1109/BigData.2015.7364090",

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T1 - Predicting various types of user attributes in Twitter by using personalized pagerank

AU - Uesato, Kazuya

AU - Asai, Hiroki

AU - Yamana, Hayato

PY - 2015/12/22

Y1 - 2015/12/22

N2 - Predicting various types of user-attributes in social networks has become indispensable for personalizing applications since there are many non-disclosed attributes in social networks. However, extracted attributes in existing works are limited to pre-defined types of attributes, which results in no extraction of unexpected-types of attributes. In this paper, we therefore propose a novel method that extracts various, i.e., unlimited, types of attributes by adopting personalized PageRank to a large social network. The experimental results using over 7.9 million of Japanese Twitter-users show that our proposed method successfully extracts four types of attributes per-user in average with 0.841 of MAP@20.

AB - Predicting various types of user-attributes in social networks has become indispensable for personalizing applications since there are many non-disclosed attributes in social networks. However, extracted attributes in existing works are limited to pre-defined types of attributes, which results in no extraction of unexpected-types of attributes. In this paper, we therefore propose a novel method that extracts various, i.e., unlimited, types of attributes by adopting personalized PageRank to a large social network. The experimental results using over 7.9 million of Japanese Twitter-users show that our proposed method successfully extracts four types of attributes per-user in average with 0.841 of MAP@20.

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KW - social networking service

KW - user attribute prediction

KW - user profiling

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T3 - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

SP - 2825

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BT - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

A2 - Luo, Feng

A2 - Ogan, Kemafor

A2 - Zaki, Mohammed J.

A2 - Haas, Laura

A2 - Ooi, Beng Chin

A2 - Kumar, Vipin

A2 - Rachuri, Sudarsan

A2 - Pyne, Saumyadipta

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A2 - Hu, Xiaohua

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 29 October 2015 through 1 November 2015

ER -

Predicting various types of user attributes in Twitter by using personalized pagerank

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