Quantifying the differences incall detail records

Federico Botta

doi:10.1098/rsos.201443

Abstract

The increasing availability of mobile phone data has attracted the attention of several researchers interested in studying our collective behaviour. Our interactions with the phone network can take several forms, from SMS messages to phone calls and data usage. Typically, mobile phone data are released to researchers in the form of call detail records, which contain records of different types of interactions, and can be used to analyse various aspects of our behaviour. However, the inherently behavioural nature of these interactions may result in differences between how we make phone calls and receive text messages. Studies which rely on data derived from these interactions, therefore, need to carefully consider these differences. Here, we aim to investigate differences and limitations of different types of mobile phone interactions data by analysing a large mobile phone dataset. We study the relationship between different types of interactions and show how it changes over time. We anticipate our findings to be of interest to all researchers working in the area of computational social science.

Highlights

Recent years have witnessed a surge in the use of new forms of data to study our society [1,2,3,4]
Each cell has five values corresponding to the five call detail records (CDR) layers under analysis
We calculate Kendall’s correlation coefficient for each pair of layers and we present the results in figure 2. This initial analysis suggests that there is a strong relationship between all the CDR layers when the data are aggregated over a long period of time

Summary

Introduction

Recent years have witnessed a surge in the use of new forms of data to study our society [1,2,3,4]. An even more popular data source, which has quickly become fundamental in the study of human behaviour, is that derived from our interactions with the mobile phone network [15,16,17,18]. Such data have become a key part in the new field of computational social science and has recently been of great importance in studying the current COVID-19 pandemic [19,20,21,22,23,24].

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Results

Conclusion