Big Data for Social Sciences: Measuring patterns of human behavior through large-scale mobile phone data

📝 Abstract

Through seven publications this dissertation shows how anonymized mobile phone data can contribute to the social good and provide insights into human behaviour on a large scale. The size of the datasets analysed ranges from 500 million to 300 billion phone records, covering millions of people. The key contributions are two-fold: 1. Big Data for Social Good: Through prediction algorithms the results show how mobile phone data can be useful to predict important socio-economic indicators, such as income, illiteracy and poverty in developing countries. Such knowledge can be used to identify where vulnerable groups in society are, reduce economic shocks and is a critical component for monitoring poverty rates over time. Further, the dissertation demonstrates how mobile phone data can be used to better understand human behaviour during large shocks in society, exemplified by an analysis of data from the terror attack in Norway and a natural disaster on the south-coast in Bangladesh. This work leads to an increased understanding of how information spreads, and how millions of people move around. The intention is to identify displaced people faster, cheaper and more accurately than existing survey-based methods. 2. Big Data for efficient marketing: Finally, the dissertation offers an insight into how anonymised mobile phone data can be used to map out large social networks, covering millions of people, to understand how products spread inside these networks. Results show that by including social patterns and machine learning techniques in a large-scale marketing experiment in Asia, the adoption rate is increased by 13 times compared to the approach used by experienced marketers. A data-driven and scientific approach to marketing, through more tailored campaigns, contributes to less irrelevant offers for the customers, and better cost efficiency for the companies.

💡 Analysis

Through seven publications this dissertation shows how anonymized mobile phone data can contribute to the social good and provide insights into human behaviour on a large scale. The size of the datasets analysed ranges from 500 million to 300 billion phone records, covering millions of people. The key contributions are two-fold: 1. Big Data for Social Good: Through prediction algorithms the results show how mobile phone data can be useful to predict important socio-economic indicators, such as income, illiteracy and poverty in developing countries. Such knowledge can be used to identify where vulnerable groups in society are, reduce economic shocks and is a critical component for monitoring poverty rates over time. Further, the dissertation demonstrates how mobile phone data can be used to better understand human behaviour during large shocks in society, exemplified by an analysis of data from the terror attack in Norway and a natural disaster on the south-coast in Bangladesh. This work leads to an increased understanding of how information spreads, and how millions of people move around. The intention is to identify displaced people faster, cheaper and more accurately than existing survey-based methods. 2. Big Data for efficient marketing: Finally, the dissertation offers an insight into how anonymised mobile phone data can be used to map out large social networks, covering millions of people, to understand how products spread inside these networks. Results show that by including social patterns and machine learning techniques in a large-scale marketing experiment in Asia, the adoption rate is increased by 13 times compared to the approach used by experienced marketers. A data-driven and scientific approach to marketing, through more tailored campaigns, contributes to less irrelevant offers for the customers, and better cost efficiency for the companies.

📄 Content

Measuring patterns of human behaviour through large-scale mobile phone data Big Data for social sciences Pål Sundsøy Doctor Philosophiae Faculty of Mathematics and Natural Sciences Department of Informatics UNIVERSITY OF OSLO February 2017 © Pål Sundsøy, 2017 Series of dissertations submitted to the Faculty of Mathematics and Natural Sciences, University of Oslo No. 1815 ISSN 1501-7710 All rights reserved. No part of this publication may be reproduced or transmitted, in any form or by any means, without permission.
Cover: Hanne Baadsgaard Utigard. Print production: Reprosentralen, University of Oslo.

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Human migration patterns in Bangladesh, derived from mobility patterns in mobile phone datasets. Visualisation by Pål Sundsøy.



List of publications

1. Can mobile usage predict illiteracy in a developing country?
   Preprint available at arXiv:1607.01337 [cs.AI]. 2016.

2. Deep learning applied to mobile phone data for Individual income classification
   Joint work with Bjelland, J., Reme B.A., Iqbal A. and Jahani, E. Published in International conference on Artificial Intelligence: Technologies and Applications (ICAITA). Atlantic Press. 2016.

3. Mapping Poverty using mobile phone and satellite data
   Joint work with Steele, J.E., Pezzulo, C., Alegana, V., Bird, T., Blumenstock, J., Bjelland J., Engø-Monsen, K., de Montjoye, Y.A., Iqbal, A., Hadiuzzaman, K., Lu, X., Wetter, E., Tatem, A. and Bengtsson, L.
   Published in Journal of The Royal Society Interface 14:20160690. 2017

4. The activation of core social networks in the wake of the 22 July Oslo bombing Joint work with Ling, R., Engø-Monsen, K., Bjelland, J. and Canright, G. Published in Social Networks Analysis and Mining ASONAM (pp. 586-590). 2012.

5. Detecting climate adaptation with mobile network data: Anomalies in communication, mobillity and consumption patterns during Cyclone Mahasen Joint work with Lu, X., Wrathall, D., Nadiruzzaman, M., Wetter, E., Iqbal, A., Qureshi, T., Tatem, A., Canright, G., Engø-Monsen, K. and Bengtsson, L. Published in Climatic Change, 138(3-4), pp.505-519. 2016.

6. Comparing and visualizing the social spreading of products on a large-scale social network
   Joint work with Bjelland, J., Engø-Monsen, K., Canright, G. and Ling, R. Published in Influence on Technology on Social Network Analysis and Mining, Tanzel Ozyer et. al.
   Springer International Publishing. 2012.

7. Big Data-Driven Marketing: How Machine Learning outperforms marketers’ gut-feeling
   Joint work with Bjelland, J., Iqbal, A., Pentland, A. and de Montjoye, Y.A. Published in International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (pp. 367-374). Springer International Publishing. 2014.

 

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8. Impact of human mobility on the emergence of dengue epidemics in Pakistan +%*0
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Wesolowski, A., Qureshi, T., Boni, M.F., Johansson, M.A., Rasheed, S.B., Engø-Monsen, K. and Buckee, C.O. Published in Proceedings of the National Academy of Sciences, 112(38):11887-92. 2015. 9. Improving official statistics in emerging markets using machine learning and mobile phone data Joint work with Jahani, E., Bengtsson, L., Bjelland, J., Pentland, A. and de Montjoye, Y.A. In review, EPJ Data Science. 2017. 10. Unveiling Hidden Migration and Mobility Patterns in Climate Stressed Regions: A Longitudinal Study of Six Million Anonymous Mobile Phone Users in Bangladesh +%*0
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Lu, X., Wrathall, D.J., Nadiruzzaman, M., Wetter, E., Iqbal, A., Qureshi, T., Tatem, A., Canright, G., Engø-Monsen, K. and Bengtsson, L. Published in Global Environmental Change 38, pp.1-7. 2016. 11. Small and Even Smaller Circles: The Size of Mobile Phone-Based Core Social Networks in Scandinavia and South Asia Joint work with Ling, R., Canright, G., Bjelland, J. and Engø-Monsen, K. Published in Journal of Intercultural Communication Research 41(3), pp.320-339. 2012. 12. Joy of Giving: Increasing Product Uptake by allowing customers to Forward Joint work with Bjelland, J., Canright, G., Iqbal, A., Grønnetvet, G., Norton, M. and Reme, B.A. Article in preparation. 13. Handset-centric view of smartphone application use Joint work with Rana, J., Bjelland, J., Couronne, T., Wagner, D. and Rice, A. Published in Procedia Computer Science, 34, pp.368-375. 2014. 14. Small circles: Mobile Telephony and the cultivation of the private Joint work with Ling, R., Bjelland, J. and Campbell, S. Published in The Information Society, 30(4), pp.282-291. 2014.
15. The socio-demographics of texting: An analysis of traffic data Joint work with Ling, R. and Bertel, T. Published in New Media & Society, 14(2), pp.281-298. 2012.

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