Channel-Specific Daily Patterns in Mobile Phone Communication
- 484 Downloads
Humans follow circadian rhythms, visible in their activity levels as well as physiological and psychological factors. Such rhythms are also visible in electronic communication records, where the aggregated activity levels of e.g. mobile telephone calls or Wikipedia edits are known to follow their own daily patterns. Here, we study the daily communication patterns of 24 individuals over 18 months, and show each individual has a different, persistent communication pattern. These patterns may differ for calls and text messages, which points towards calls and texts serving a different role in communication. For both calls and texts, evenings play a special role. There are also differences in the daily patterns of males and females both for calls and texts, both in how they communicate with individuals of the same gender versus opposite gender, and also in how communication is allocated at social ties of different nature (kin ties vs. non-kin ties). Taken together, our results show that there is an unexpected richness to the daily communication patterns, from different types of ties being activated at different times of day to different roles of channels and gender differences.
KeywordsCircadian Rhythm Text Message Relative Entropy Reference Distance Daily Pattern
TA and JS acknowledge support from the Academy of Finland, project “Temporal networks of human interactions” (no. 260427), and computational resources by Aalto Science IT. RD’s research is supported by an ERC Advanced grant. SGBR and RD acknowledge support from the UK EPSRC and ESRC research councils for collecting the data.
- 5.Yasseri, T., Quattrone, G., Mashhadi, A.: Temporal analysis of activity patterns of editors in collaborative mapping project of openstreetmap. In: Proceedings of the 9th International Symposium on Open Collaboration, p. 13. ACM (2013)Google Scholar
- 7.Louail, T., et al.: From mobile phone data to the spatial structure of cities. Sci. Rep. 4 (2014)Google Scholar
- 8.ten Thij, M., Bhulai, S., Kampstra, P.: Circadian patterns in twitter. In: Data Analytics 2014, The Third International Conference on Data Analytics, pp. 12–17 (2014)Google Scholar
- 11.Ling, R.: The Mobile Connection: The Cell Phone’s Impact on Society. Morgan Kaufmann (2004)Google Scholar
- 16.Palchykov, V., Kaski, K., Kertész, J., Barabási, A.-L., Dunbar, R.: Sex differences in intimate relationships. Sci. Rep. 2 (2012)Google Scholar