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Understanding User Attributes from Calling Behavior: Exploring Call Detail Records through Field Observations

Authors:

Apichon Witayangkurn,

Hiroshi Kanasugi,

Teerayut Horanont,

Ryosuke ShibasakiAuthors Info & Claims

MoMM '14: Proceedings of the 12th International Conference on Advances in Mobile Computing and Multimedia

Pages 95 - 104

https://doi.org/10.1145/2684103.2684107

Published: 08 December 2014 Publication History

Abstract

Mobile phones are arguably one of the most prolific sources of large-scale human mobility data. The availability of this data has generated a massive body of research focused on understanding the dynamics and patterns of human mobility. However, it is increasingly evident that additional value can be derived from such data. This paper proposes a novel approach for understanding the attributes of mobile users by analyzing calling behavior derived from field survey data, in combination with call detail records (CDRs). Our survey reveals distinctive traits in calling behavior that correspond to user attributes. Analysis results demonstrate that frequent call locations, the variability in call time distributions, and the locations from which calls are made around midday are all keys to distinguishing gender. In addition, the location of calls initiated during the morning hours is a key to analyzing income levels for males.

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Cited By

Milusheva SLewin ABegazo Gomez TMatekenya DReid K(2021)Challenges and opportunities in accessing mobile phone data for COVID-19 response in developing countriesData & Policy10.1017/dap.2021.103Online publication date: 15-Sep-2021
https://doi.org/10.1017/dap.2021.10
Katranji MKraiem SMoalic LSanmarty GKhodabandelou GCaminada AHadj Selem F(2019)Deep multi-task learning for individuals origin–destination matrices estimation from census dataData Mining and Knowledge Discovery10.1007/s10618-019-00662-yOnline publication date: 12-Nov-2019
https://doi.org/10.1007/s10618-019-00662-y
Batran MMejia MKanasugi HSekimoto YShibasaki R(2018)Inferencing Human Spatiotemporal Mobility in Greater Maputo via Mobile Phone Big Data MiningISPRS International Journal of Geo-Information10.3390/ijgi70702597:7(259)Online publication date: 30-Jun-2018
https://doi.org/10.3390/ijgi7070259
Show More Cited By

Index Terms

Understanding User Attributes from Calling Behavior: Exploring Call Detail Records through Field Observations
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Other conferences

MoMM '14: Proceedings of the 12th International Conference on Advances in Mobile Computing and Multimedia

December 2014

464 pages

ISBN:9781450330084

DOI:10.1145/2684103

General Chair:
Shyue-Liang Leon Wang
National University of Kaohsiung, Taiwan
,
Program Chairs:
Yu-Hui Tao
National University of Kaohsiung, Taiwan
,
Liming Chen
University of Ulster, UK
,
Chung-Nan Lee
National Sun Yat-sen University, Taiwan

Copyright © 2014 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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JKU: Johannes Kepler Universität Linz

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Association for Computing Machinery

New York, NY, United States

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Published: 08 December 2014

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MoMM '14: The 12th International Conference on Advances in Mobile Computing and Multimedia

December 8 - 10, 2014

Kaohsiung, Taiwan

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Cited By

Milusheva SLewin ABegazo Gomez TMatekenya DReid K(2021)Challenges and opportunities in accessing mobile phone data for COVID-19 response in developing countriesData & Policy10.1017/dap.2021.103Online publication date: 15-Sep-2021
https://doi.org/10.1017/dap.2021.10
Katranji MKraiem SMoalic LSanmarty GKhodabandelou GCaminada AHadj Selem F(2019)Deep multi-task learning for individuals origin–destination matrices estimation from census dataData Mining and Knowledge Discovery10.1007/s10618-019-00662-yOnline publication date: 12-Nov-2019
https://doi.org/10.1007/s10618-019-00662-y
Batran MMejia MKanasugi HSekimoto YShibasaki R(2018)Inferencing Human Spatiotemporal Mobility in Greater Maputo via Mobile Phone Big Data MiningISPRS International Journal of Geo-Information10.3390/ijgi70702597:7(259)Online publication date: 30-Jun-2018
https://doi.org/10.3390/ijgi7070259
Katranji MSanmarty GMoalic LKraiem SCaminada ASelem F(2018)RNN Encoder-Decoder for the inference of regular human mobility patterns2018 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2018.8489639(1-9)Online publication date: Jul-2018
https://doi.org/10.1109/IJCNN.2018.8489639
Resende JSousa PAntunes L(2018)Evaluating the Privacy Properties of Secure VoIP MetadataTrust, Privacy and Security in Digital Business10.1007/978-3-319-98385-1_5(57-68)Online publication date: 27-Jul-2018
https://doi.org/10.1007/978-3-319-98385-1_5
Arai AFan ZMatekenya DShibasaki R(2016)Comparative Perspective of Human Behavior Patterns to Uncover Ownership Bias among Mobile Phone UsersISPRS International Journal of Geo-Information10.3390/ijgi50600855:6(85)Online publication date: 6-Jun-2016
https://doi.org/10.3390/ijgi5060085
Elgazzar M(2016)M2M Power Saving based on Analysis of Network Call Data RecordsProceedings of the 10th International Conference on Informatics and Systems10.1145/2908446.2908454(254-259)Online publication date: 9-May-2016
https://dl.acm.org/doi/10.1145/2908446.2908454
Katranji MThuillier EKraiem SMoalic LSelem F(2016)Mobility data disaggregation: A transfer learning approach2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC.2016.7795783(1672-1677)Online publication date: Nov-2016
https://doi.org/10.1109/ITSC.2016.7795783
Mayer JMutchler PMitchell J(2016)Evaluating the privacy properties of telephone metadataProceedings of the National Academy of Sciences10.1073/pnas.1508081113113:20(5536-5541)Online publication date: 16-May-2016
https://doi.org/10.1073/pnas.1508081113
Arai AWitayangkurn AHoranont TShao XShibasaki R(2015)Understanding the unobservable population in call detail records through analysis of mobile phone user calling behavior: A case study of Greater Dhaka in Bangladesh2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PERCOM.2015.7146530(207-214)Online publication date: Mar-2015
https://doi.org/10.1109/PERCOM.2015.7146530

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