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© 2019, The Author(s). In human relations individuals’ gender and age play a key role in the structures and dynamics of their social arrangements. In order to analyze the gender preferences of individuals in interaction with others at different stages of their lives we study a large mobile phone dataset. To do this we consider four fundamental gender-related caller and callee combinations of human interactions, namely male to male, male to female, female to male, and female to female, which together with age, kinship, and different levels of friendship give rise to a wide scope of human sociality. Here we analyse the relative strength of these four types of interaction using call detail records. Our analysis suggests strong age dependence for an individual of one gender choosing to call an individual of either gender. We observe a strong bonding with the opposite gender across most of their reproductive age. However, older women show a strong tendency to connect to another female that is one generation younger in a way that is suggestive of the grandmothering effect. We also find that the relative strength among the four possible interactions depends on phone call duration. For calls of medium and long duration, opposite gender interactions are significantly more probable than same gender interactions during the reproductive years, suggesting potential emotional exchange between spouses. By measuring the fraction of calls to other generations we find that mothers tend to make calls more to their daughters than to their sons, whereas fathers make calls more to their sons than to their daughters. For younger callers, most of their calls go to the same generation contacts, while older people call the younger people more frequently, which supports the suggestion that affection flows downward. Our study primarily rests on resolving the nature of interactions by examining the durations of calls. In addition, we analyse the intensity of the observed effects using a score based on a null model.

Original publication

DOI

10.1140/epjds/s13688-019-0185-9

Type

Journal article

Journal

EPJ Data Science

Publication Date

01/12/2019

Volume

8