Characterizing ethnic interactions from human communication patterns in Ivory Coast

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March 2015, 10(1): 87-99. doi: 10.3934/nhm.2015.10.87

Characterizing ethnic interactions from human communication patterns in Ivory Coast

Alfredo Jose Morales ^1, , Werner Creixell ^2, , Javier Borondo ^3, , Juan Carlos Losada ^4, and Rosa Maria Benito ^4,

1.	Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, ETSI Agrónomos, 28040, Madrid, Spain
2.	Departamento de Electrónica, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso
3.	Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, ETSI Agrónomos, 28040, Madrid, Spain
4.	Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, ETSI Agrónomos, 28040, Madrid, Spain, Spain

Received July 2014 Revised December 2014 Published February 2015

Abstract
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Towards the consolidation of peace and national development, Ivory Coast must overcome the lack of cohesion, responsible for the emergence of two civil wars in the last years. As in many African countries, ethnic violence is a result of the way territories are organized and the prevalence of some groups over others. Nowadays the increasing availability of electronic data allows to quantify and unveil societal relationships in an unprecedented way. In this sense, the present work analyzes mobile phone data in order to provide information about the regional and ethnic interactions in Ivory Coast. We accomplish so by means of the construction and analysis of complex social networks with several types of interactions, such as calling activity and human mobility. We found that in a subregional scale, the ethnic identity plays an important role in the communication patterns, while at the interregional scale, other factors arise like economical interests and available infrastructure.

Keywords: Data for development, complex networks, ethnic interactions, call detail records, community structure., mobility.

Mathematics Subject Classification: 91D30, 05C82, 91C2.

Citation: Alfredo Jose Morales, Werner Creixell, Javier Borondo, Juan Carlos Losada, Rosa Maria Benito. Characterizing ethnic interactions from human communication patterns in Ivory Coast. Networks and Heterogeneous Media, 2015, 10 (1) : 87-99. doi: 10.3934/nhm.2015.10.87

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[14]	A. J. Morales, J. Borondo, J. C. Losada and R. M. Benito, Efficiency of human activity on information spreading on Twitter, Social Networks, 39 (2014), 1-11. doi: 10.1016/j.socnet.2014.03.007.
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[16]	M. E. J. Newman, Mixing patterns in networks, Physical Review E, 67 (2003), 026126, 13pp. doi: 10.1103/PhysRevE.67.026126.
[17]	D. Pastor, A. J. Morales, Y. Torres, J. Bauer, A. Wadhwa, C. Castro-Correa, A. Calderón-Mariscal, L. Romanoff, J. Lee, A. Rutherford, V. Frias-Martinez, N. Oliver, E. Frias-Martinez and M. Luengo-Oroz, Flooding through the lens of mobile phone activity, in IEEE Global Humanitarian Technology Conference (GHTC), IEEE, 2014, 279-286. doi: 10.1109/GHTC.2014.6970293.
[18]	A. Pentland, Social Physics: How Good Ideas Spread. The Lessons From a New Science, Penguin Group (USA) Incorporated, 2014.
[19]	K. K. Rachuri, M. Musolesi, C. Mascolo, P. J. Rentfrow, C. Longworth and A. Aucinas, EmotionSense: A mobile phones based adaptive platform for experimental social psychology research, Proceedings of the 12th ACM International Conference on Ubiquitous Computing (New York, NY, USA), Ubicomp '10, ACM, 2010, 281-290. doi: 10.1145/1864349.1864393.
[20]	T. Sakaki, M. Okazaki and Y. Matsuo, Earthquake shakes twitter users: Real-time event detection by social sensors, Proceedings of the 19th International Conference on World Wide Web (New York, NY, USA), WWW '10, ACM, 2010, 851-860. doi: 10.1145/1772690.1772777.
[21]	J. L. Toole, M. Ulm, M. C González and D. Bauer, Inferring land use from mobile phone activity, in Proceedings of the ACM SIGKDD International Workshop on Urban Computing, ACM, 2012, 1-8. doi: 10.1145/2346496.2346498.
[22]	A. Wesolowski, et al., Quantifying the impact of human mobility on malaria, Science, 338 (2012), 267-270.
[23]	, =, ().

show all references

References:

[1]	, Annual Report 2011/2012,, Report from International Cocoa Organization (ICCO)., ().
[2]	L. Bengtsson, X. Lu, A. Thorson, R. Garfield and J. von Schreeb, Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: A post-earthquake geospatial study in Haiti, PLoS Medicine, 8 (2011), e1001083.
[3]	V. D. Blondel et al., Data for development: The D4D challenge on mobile phone data, arXiv:1210.0137, 2012.
[4]	V. D. Blondel, J.-L. Guillaume, R. Lambiotte and E. Lefebvre, Fast unfolding of communities in large networks, J. Stat. Mech., 10 (2008), P10008.
[5]	S. Bocaletti, V. Latora, Y. Moreno, M. Chavez and D.-U. Hwang, Complex networks: Structure and dynamics, Phys. Rep., 424 (2006), 175-308. doi: 10.1016/j.physrep.2005.10.009.
[6]	J. Bollen, H. Mao and X.-J. Zeng, Twitter mood predicts the stock market, J. Comput. Science, 2 (2011), 1-8. doi: 10.1016/j.jocs.2010.12.007.
[7]	, Cote d'Ivoire Roads Shapefiles, Service from African Development Bank., Available from: , ().
[8]	Document de strategie pays et programe indicatif national pour la periode 2008-2013, Report from Communaute europeenne - Republique de Côte d'Ivoire, 2008., Available from: , ().
[9]	N. Eagle, M. Macy and R. Claxton, Network diversity and economic development, Science, 328 (2010), 1029-1031. doi: 10.1126/science.1186605.
[10]	T. Hastie, et al., The Elements of Statistical Learning, Springer, New York, 2009. doi: 10.1007/978-0-387-84858-7.
[11]	D. Lazer, et al., Life in the network: The coming age of computational social science, Science, 323 (2009), 721-723.
[12]	M. P. Lewis, Ethnologue: Languages of the World, $16^{th}$ edition, SIL International, Dallas, Tex., 2009.
[13]	M. Lim, R. Metzler and Y. Bar-Yam, Global pattern formation and ethnic/cultural violence, Science, 317 (2007), 1540-1544. doi: 10.1126/science.1142734.
[14]	A. J. Morales, J. Borondo, J. C. Losada and R. M. Benito, Efficiency of human activity on information spreading on Twitter, Social Networks, 39 (2014), 1-11. doi: 10.1016/j.socnet.2014.03.007.
[15]	M. E. J. Newman, Modularity and community structure in networks, Phys. Rev. E, 103 (2006), 8577-8582. doi: 10.1073/pnas.0601602103.
[16]	M. E. J. Newman, Mixing patterns in networks, Physical Review E, 67 (2003), 026126, 13pp. doi: 10.1103/PhysRevE.67.026126.
[17]	D. Pastor, A. J. Morales, Y. Torres, J. Bauer, A. Wadhwa, C. Castro-Correa, A. Calderón-Mariscal, L. Romanoff, J. Lee, A. Rutherford, V. Frias-Martinez, N. Oliver, E. Frias-Martinez and M. Luengo-Oroz, Flooding through the lens of mobile phone activity, in IEEE Global Humanitarian Technology Conference (GHTC), IEEE, 2014, 279-286. doi: 10.1109/GHTC.2014.6970293.
[18]	A. Pentland, Social Physics: How Good Ideas Spread. The Lessons From a New Science, Penguin Group (USA) Incorporated, 2014.
[19]	K. K. Rachuri, M. Musolesi, C. Mascolo, P. J. Rentfrow, C. Longworth and A. Aucinas, EmotionSense: A mobile phones based adaptive platform for experimental social psychology research, Proceedings of the 12th ACM International Conference on Ubiquitous Computing (New York, NY, USA), Ubicomp '10, ACM, 2010, 281-290. doi: 10.1145/1864349.1864393.
[20]	T. Sakaki, M. Okazaki and Y. Matsuo, Earthquake shakes twitter users: Real-time event detection by social sensors, Proceedings of the 19th International Conference on World Wide Web (New York, NY, USA), WWW '10, ACM, 2010, 851-860. doi: 10.1145/1772690.1772777.
[21]	J. L. Toole, M. Ulm, M. C González and D. Bauer, Inferring land use from mobile phone activity, in Proceedings of the ACM SIGKDD International Workshop on Urban Computing, ACM, 2012, 1-8. doi: 10.1145/2346496.2346498.
[22]	A. Wesolowski, et al., Quantifying the impact of human mobility on malaria, Science, 338 (2012), 267-270.
[23]	, =, ().

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