research-article

Steeler nation, 12th man, and boo birds: classifying Twitter user interests using time series

Authors:

Su YanAuthors Info & Claims

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 684 - 691

https://doi.org/10.1145/2492517.2492551

Published: 25 August 2013 Publication History

Abstract

The problem of Twitter user classification using the contents of tweets is studied. We generate time series from tweets by exploiting the latent temporal information and solve the classification problem in time series domain. Our approach is inspired by the fact that Twitter users sometimes exhibit the periodicity pattern when they share their activities or express their opinions. We apply our proposed methods to both binary and multi-class classification of sports and political interests of Twitter users and compare the performance against eight conventional classification methods using textual features. Experimental results using 2.56 million tweets show that our best binary and multi-class approaches improve the classification accuracy over the best baseline binary and multi-class approaches by 15% and 142%, respectively.

References

[1]

M. Pennacchiotti and A. M. Popescu, "Democrats, republicans and starbucks afficionados: User classification in twitter," in KDD, 2011.

Digital Library

[2]

D. Rao, D. Yarowsky, A. Shreevats, and M. Gupta, "Classifying latent user attributes in twitter," in SMUC, 2010.

Digital Library

[3]

A. Java, X. Song, T. Finin, and B. L. Tseng, "Why we twitter: An analysis of a microblogging community," in WebKDD, 2007.

[4]

B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, and M. Demirbas, "Short text classification in twitter to improve information filtering," in SIGIR, 2010.

Digital Library

[5]

X. H. Phan, M. L. Nguyen, and S. Horiguchi, "Learning to classify short and sparse text & web with hidden topics from large-scale data collections," in WWW, 2008.

Digital Library

[6]

A. Sun, "Short text classification using very few words," in SIGIR, 2012.

Digital Library

[7]

K. Nishida, T. Hoshide, and K. Fujimura, "Improving tweet stream classification by detecting changes in word probability," in SIGIR, 2012.

Digital Library

[8]

J. D. Burger, J. C. Henderson, G. Kim, and G. Zarrella, "Discriminating gender on twitter," in EMNLP, 2011.

Digital Library

[9]

A. Bifet and E. Frank, "Sentiment knowledge discovery in twitter streaming data," in DS, 2010.

Digital Library

[10]

Z. Cheng, J. Caverlee, and K. Lee, "You are where you tweet: a content-based approach to geo-locating twitter users," in CIKM, 2010.

Digital Library

[11]

Z. Cheng, J. Caverlee, K. Lee, and D. Z. Sui, "Exploring millions of footprints in location sharing services," in ICWSM, 2011.

[12]

H.-W. Chang, D. Lee, M. Eltaher, and J. Lee, "@phillies tweeting from philly? predicting twitter user locations with spatial word usage," in ASONAM, 2012.

Digital Library

[13]

F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida, "Detecting spammers on twitter," in CEAS, 2010.

[14]

K. Radinsky, E. Agichtein, E. Gabrilovich, and S. Markovitch, "A word at a time: computing word relatedness using temporal semantic analysis," in WWW, 2011.

Digital Library

[15]

J. Yang and J. Leskovec, "Patterns of temporal variation in online media," in WSDM, 2011.

Digital Library

[16]

M. Pennacchiotti and A.-M. Popescu, "A machine learning approach to twitter user classification," in ICWSM, 2011.

[17]

D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," in Journal of Machine Learning Research, 2003.

Digital Library

[18]

C. D. Manning and H. Schutze, Foundations of statistical natural language processing. MIT Press, 1999.

Digital Library

[19]

N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.

Digital Library

[20]

X. Xi, E. J. Keogh, C. R. Shelton, L. Wei, and C. A. Ratanamahatana, "Fast time series classification using numerosity reduction," in ICML, 2006.

Digital Library

[21]

S. D. Bay, "Combining nearest neighbor classifiers through multiple feature subsets," in ICML, 1998.

Digital Library

[22]

D. J. Berndt and J. Clifford, "Using dynamic time warping to find patterns in time series," in KDD, 1994.

[23]

J. Shieh and E. J. Keogh, "isax: indexing and mining terabyte sized time series," in KDD, 2008.

Digital Library

[24]

M. Conover, J. Ratkiewicz, M. Francisco, B. Gonçalves, F. Menczer, and A. Flammini, "Political polarization on twitter," in ICWSM, 2011.

Cited By

Al-Qurishi MAlhuzami SAlrubaian MHossain MAlamri ARahman M(2018)User profiling for big social media data using standing ovation modelMultimedia Tools and Applications10.1007/s11042-017-5402-677:9(11179-11201)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s11042-017-5402-6
Guzman EAlkadhi RSeyff N(2017)An exploratory study of Twitter messages about software applicationsRequirements Engineering10.1007/s00766-017-0274-x22:3(387-412)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s00766-017-0274-x
Cao YWang SLi XCao CLiu YTan J(2017)Inferring Social Network User’s Interest Based on Convolutional Neural NetworkNeural Information Processing10.1007/978-3-319-70139-4_67(657-666)Online publication date: 29-Oct-2017
https://doi.org/10.1007/978-3-319-70139-4_67
Show More Cited By

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Steeler nation, 12th man, and boo birds: classifying Twitter user interests using time series

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cover image ACM Conferences

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2013

1558 pages

ISBN:9781450322409

DOI:10.1145/2492517

General Chairs:
Jon Rokne
University of Calgary, Calgary, AB, Canada
,
Christos Faloutsos
Carnegie Mellon University, Pittsburgh, PA

Copyright © 2013 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: 25 August 2013

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ASONAM '13: Advances in Social Networks Analysis and Mining 2013

August 25 - 28, 2013

Ontario, Niagara, Canada

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Al-Qurishi MAlhuzami SAlrubaian MHossain MAlamri ARahman M(2018)User profiling for big social media data using standing ovation modelMultimedia Tools and Applications10.1007/s11042-017-5402-677:9(11179-11201)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s11042-017-5402-6
Guzman EAlkadhi RSeyff N(2017)An exploratory study of Twitter messages about software applicationsRequirements Engineering10.1007/s00766-017-0274-x22:3(387-412)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s00766-017-0274-x
Cao YWang SLi XCao CLiu YTan J(2017)Inferring Social Network User’s Interest Based on Convolutional Neural NetworkNeural Information Processing10.1007/978-3-319-70139-4_67(657-666)Online publication date: 29-Oct-2017
https://doi.org/10.1007/978-3-319-70139-4_67
Litou IKalogeraki VGunopulos D(2016)On Topic Aware Recommendation to Increase Popularity in Microblogging Services (Short Paper)On the Move to Meaningful Internet Systems: OTM 2016 Conferences10.1007/978-3-319-48472-3_40(673-681)Online publication date: 18-Oct-2016
https://doi.org/10.1007/978-3-319-48472-3_40
Fan RZhao JXu K(2015)Topic dynamics in Weibo: a comprehensive studySocial Network Analysis and Mining10.1007/s13278-015-0282-05:1Online publication date: 14-Jul-2015
https://doi.org/10.1007/s13278-015-0282-0
Tagarelli AInterdonato R(2015)Time-aware analysis and ranking of lurkers in social networksSocial Network Analysis and Mining10.1007/s13278-015-0276-y5:1Online publication date: 11-Aug-2015
https://doi.org/10.1007/s13278-015-0276-y
Raghuram MAkshay KChandrasekaran K(2015)Efficient User Profiling in Twitter Social Network Using Traditional ClassifiersIntelligent Systems Technologies and Applications10.1007/978-3-319-23258-4_35(399-411)Online publication date: 22-Aug-2015
https://doi.org/10.1007/978-3-319-23258-4_35
Lo SCornforth DChiong R(2015)Effects of Training Datasets on Both the Extreme Learning Machine and Support Vector Machine for Target Audience Identification on TwitterProceedings of ELM-2014 Volume 110.1007/978-3-319-14063-6_35(417-434)Online publication date: 2015
https://doi.org/10.1007/978-3-319-14063-6_35

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