#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Link-centric analysis of variation by demographics in mobile phone communication patterns


Autoři: Mikaela Irene D. Fudolig aff001;  Kunal Bhattacharya aff002;  Daniel Monsivais aff003;  Hang-Hyun Jo aff001;  Kimmo Kaski aff003
Působiště autorů: Asia Pacific Center for Theoretical Physics, Pohang, Republic of Korea aff001;  Department of Industrial Engineering and Management, Aalto University School of Science, Espoo, Finland aff002;  Department of Computer Science, Aalto University School of Science, Espoo, Finland aff003;  Department of Physics, Pohang University of Science and Technology, Pohang, Republic of Korea aff004;  The Alan Turing Institute, London, England, United Kingdom aff005
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0227037

Souhrn

We present a link-centric approach to study variation in the mobile phone communication patterns of individuals. Unlike most previous research on call detail records that focused on the variation of phone usage across individual users, we examine how the calling and texting patterns obtained from call detail records vary among pairs of users and how these patterns are affected by the nature of relationships between users. To demonstrate this link-centric perspective, we extract factors that contribute to the variation in the mobile phone communication patterns and predict demographics-related quantities for pairs of users. The time of day and the channel of communication (calls or texts) are found to explain most of the variance among pairs that frequently call each other. Furthermore, we find that this variation can be used to predict the relationship between the pairs of users, as inferred from their age and gender, as well as the age of the younger user in a pair. From the classifier performance across different age and gender groups as well as the inherent class overlap suggested by the estimate of the bounds of the Bayes error, we gain insights into the similarity and differences of communication patterns across different relationships.

Klíčová slova:

Communications – Social communication – Behavior – Age groups – Machine learning – Cell phones – Forecasting – Principal component analysis


Zdroje

1. Blondel VD, Decuyper A, Krings G. A survey of results on mobile phone datasets analysis. EPJ Data Science. 2015;4(1):10. doi: 10.1140/epjds/s13688-015-0046-0

2. Coviello L, Sohn Y, Kramer ADI, Marlow C, Franceschetti M, Christakis NA, et al. Detecting Emotional Contagion in Massive Social Networks. PLoS ONE. 2014;9(3):e90315. doi: 10.1371/journal.pone.0090315 24621792

3. Golder SA, Macy MW. Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures. Science. 2011;333(6051):1878–1881. doi: 10.1126/science.1202775 21960633

4. Bhattacharya K, Kaski K. Social physics: uncovering human behaviour from communication. Advances in Physics: X. 2019;4(1):1527723.

5. Migheli M. The sibling effect on the consumption of phone services. International Journal of Consumer Studies. 2016;40(3):319–326. doi: 10.1111/ijcs.12258

6. Smoreda Z, Licoppe C. Gender-Specific Use of the Domestic Telephone. Social Psychology Quarterly. 2000;63(3):238–252. doi: 10.2307/2695871

7. Friebel G, Seabright P. Do women have longer conversations? Telephone evidence of gendered communication strategies. Journal of Economic Psychology. 2011;32(3):348–356. doi: 10.1016/j.joep.2010.12.008

8. Aledavood T, López E, Roberts SGB, Reed-Tsochas F, Moro E, Dunbar RIM, et al. Daily Rhythms in Mobile Telephone Communication. PLOS ONE. 2015;10(9):e0138098. doi: 10.1371/journal.pone.0138098 26390215

9. Kovanen L, Kaski K, Kertész J, Saramäki J. Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences. Proceedings of the National Academy of Sciences. 2013;110(45):18070–18075. doi: 10.1073/pnas.1307941110

10. Zainudeen A, Iqbal T, Samarajiva R. Who’s got the phone? Gender and the use of the telephone at the bottom of the pyramid. New Media & Society. 2010;12(4):549–566. doi: 10.1177/1461444809346721

11. Mehrotra A, Nguyen A, Blumenstock J, Mohan V. Differences in Phone Use Between Men and Women: Quantitative Evidence from Rwanda. In: Proceedings of the Fifth International Conference on Information and Communication Technologies and Development. ICTD’12. New York, NY, USA: ACM; 2012. p. 297–306. Available from: http://doi.acm.org/10.1145/2160673.2160710.

12. Kunal Bhattacharya, Asim Ghosh, Daniel Monsivais, Dunbar Robin IM, Kimmo Kaski. Sex differences in social focus across the life cycle in humans. Royal Society Open Science. 2016;3(4):160097. doi: 10.1098/rsos.160097

13. Butt S, Phillips JG. Personality and self reported mobile phone use. Computers in Human Behavior. 2008;24(2):346–360. doi: 10.1016/j.chb.2007.01.019

14. Demirhan E, Randler C, Horzum MB. Is problematic mobile phone use explained by chronotype and personality? Chronobiology International. 2016;33(7):821–831. doi: 10.3109/07420528.2016.1171232 27128819

15. Lee S, Tam CL, Chie QT. Mobile Phone Usage Preferences: The Contributing Factors of Personality, Social Anxiety and Loneliness. Social Indicators Research. 2014;118(3):1205–1228. doi: 10.1007/s11205-013-0460-2

16. de Montjoye YA, Quoidbach J, Robic F, Pentland AS. Predicting Personality Using Novel Mobile Phone-Based Metrics. In: Greenberg AM, Kennedy WG, Bos ND, editors. Social Computing, Behavioral-Cultural Modeling and Prediction. Lecture Notes in Computer Science. Springer Berlin Heidelberg; 2013. p. 48–55.

17. Frias-Martinez V, Frias-Martinez E, Oliver N. A Gender-Centric Analysis of Calling Behavior in a Developing Economy Using Call Detail Records. In: AAAI Spring Symposium Series; 2010. p. 6.

18. Sarraute C, Blanc P, Burroni J. A study of age and gender seen through mobile phone usage patterns in Mexico. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014). China: IEEE; 2014. p. 836–843. Available from: http://ieeexplore.ieee.org/document/6921683/.

19. Jahani E, Sundsøy P, Bjelland J, Bengtsson L, Pentland AS, Montjoye YAd. Improving official statistics in emerging markets using machine learning and mobile phone data. EPJ Data Science. 2017;6(1):3. doi: 10.1140/epjds/s13688-017-0106-8

20. Al-Zuabi IM, Jafar A, Aljoumaa K. Predicting customer’s gender and age depending on mobile phone data. Journal of Big Data. 2019;6(1):18. doi: 10.1186/s40537-019-0180-9

21. Dong Y, Yang Y, Tang J, Yang Y, Chawla NV. Inferring User Demographics and Social Strategies in Mobile Social Networks. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD’14. New York, NY, USA: ACM; 2014. p. 15–24. Available from: http://doi.acm.org/10.1145/2623330.2623703.

22. Sarraute C, Brea J, Burroni J, Blanc P. Inference of demographic attributes based on mobile phone usage patterns and social network topology. Social Network Analysis and Mining. 2015;5(1):39. doi: 10.1007/s13278-015-0277-x

23. Yi Wang, Hui Zang, Faloutsos M. Inferring cellular user demographic information using homophily on call graphs. In: 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Turin: IEEE; 2013. p. 211–216. Available from: http://ieeexplore.ieee.org/document/6562897/.

24. Herrera-Yagüe C, Zufiria PJ. Prediction of Telephone User Attributes Based on Network Neighborhood Information. In: Perner P, editor. Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Computer Science. Springer Berlin Heidelberg; 2012. p. 645–659.

25. Felbo B, Sundsøy P, Pentland AS, Lehmann S, de Montjoye YA. Modeling the Temporal Nature of Human Behavior for Demographics Prediction. In: Altun Y, Das K, Mielikäinen T, Malerba D, Stefanowski J, Read J, et al., editors. Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. Springer International Publishing; 2017. p. 140–152.

26. Kim H, Kim GJ, Park HW, Rice RE. Configurations of Relationships in Different Media: FtF, Email, Instant Messenger, Mobile Phone, and SMS. Journal of Computer-Mediated Communication. 2007;12(4):1183–1207. doi: 10.1111/j.1083-6101.2007.00369.x

27. Wajcman J, Bittman M, Brown JE. Families without Borders: Mobile Phones, Connectedness and Work-Home Divisions. Sociology. 2008;42(4):635–652.

28. Bhattacharya K, Ghosh A, Monsivais D, Dunbar R, Kaski K. Absence makes the heart grow fonder: social compensation when failure to interact risks weakening a relationship. EPJ Data Science. 2017;6(1):1.

29. Ghosh A, Monsivais D, Bhattacharya K, Dunbar RIM, Kaski K. Quantifying gender preferences in human social interactions using a large cellphone dataset. EPJ Data Science. 2019;8(1):9. doi: 10.1140/epjds/s13688-019-0185-9

30. David-Barrett T, Kertesz J, Rotkirch A, Ghosh A, Bhattacharya K, Monsivais D, et al. Communication with Family and Friends across the Life Course. PLOS ONE. 2016;11(11):e0165687. doi: 10.1371/journal.pone.0165687 27893748

31. Fudolig MID, Monsivais D, Bhattacharya K, Jo HH, Kaski K. Different patterns of social closeness observed in mobile phone communication. Journal of Computational Social Science. 2019. doi: 10.1007/s42001-019-00054-8

32. Phithakkitnukoon S, Dantu R. Mobile Social Closeness and Communication Patterns. In: 2010 7th IEEE Consumer Communications and Networking Conference. Las Vegas, NV, USA: IEEE; 2010. p. 1–5. Available from: http://ieeexplore.ieee.org/document/5421787/.

33. Eurostat. Eurostat Data on Population (Demography, Migration and Projections);. Available from: https://ec.europa.eu/eurostat/web/population-demography-migration-projections/data.

34. Monsivais D, Ghosh A, Bhattacharya K, Dunbar RIM, Kaski K. Tracking urban human activity from mobile phone calling patterns. PLOS Computational Biology. 2017;13(11):e1005824. doi: 10.1371/journal.pcbi.1005824 29161270

35. Zhou WX, Sornette D, Hill RA, Dunbar RIM. Discrete hierarchical organization of social group sizes. Proceedings of the Royal Society B: Biological Sciences. 2005;272(1561):439–444. doi: 10.1098/rspb.2004.2970 15734699

36. Roberts SGB, Dunbar RIM, Pollet TV, Kuppens T. Exploring variation in active network size: Constraints and ego characteristics. Social Networks. 2009;31(2):138–146. doi: 10.1016/j.socnet.2008.12.002

37. Tumer K, Ghosh J. Estimating the Bayes error rate through classifier combining. In: Proceedings of 13th International Conference on Pattern Recognition. vol. 2; 1996. p. 695–699 vol.2.

38. Cover TM, Hart PE. Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13(1):21–27. doi: 10.1109/TIT.1967.1053964

39. Jolliffe IT. Principal Component Analysis. 2nd ed. Springer Series in Statistics. New York: Springer-Verlag; 2002. Available from: https://www.springer.com/gp/book/9780387954424.

40. Platt JC. Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. In: Advances in Large Margin Classifiers. MIT Press; 1999. p. 61–74.

41. Palchykov V, Kaski K, Kertész J, Barabási AL, Dunbar RIM. Sex differences in intimate relationships. Scientific Reports. 2012;2:370. doi: 10.1038/srep00370 22518274

42. Miritello G, Lara R, Cebrian M, Moro E. Limited communication capacity unveils strategies for human interaction. Scientific Reports. 2013;3(1):1950. doi: 10.1038/srep01950 23739519

43. Jo HH, Saramäki J, Dunbar RIM, Kaski K. Spatial patterns of close relationships across the lifespan. Scientific Reports. 2014;4(1):6988. doi: 10.1038/srep06988 25384677

44. Burton-Chellew MN, Dunbar RIM. Romance and reproduction are socially costly. Evolutionary Behavioral Sciences. 2015;9(4):229–241. doi: 10.1037/ebs0000046

45. Nguyen D, Doğruöz AS, Rosé CP, de Jong F. Computational Sociolinguistics: A Survey. Computational Linguistics. 2016;42(3):537–593. doi: 10.1162/COLI_a_00258


Článok vyšiel v časopise

PLOS One


2020 Číslo 1
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Získaná hemofilie - Povědomí o nemoci a její diagnostika
nový kurz

Eozinofilní granulomatóza s polyangiitidou
Autori: doc. MUDr. Martina Doubková, Ph.D.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#