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Quantifying the differences in call detail records

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 relea...

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Detalles Bibliográficos
Autor principal: Botta, Federico
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8242929/
https://www.ncbi.nlm.nih.gov/pubmed/34234948
http://dx.doi.org/10.1098/rsos.201443
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author Botta, Federico
author_facet Botta, Federico
author_sort Botta, Federico
collection PubMed
description 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.
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spelling pubmed-82429292021-07-06 Quantifying the differences in call detail records Botta, Federico R Soc Open Sci Computer Science and Artificial Intelligence 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. The Royal Society 2021-06-30 /pmc/articles/PMC8242929/ /pubmed/34234948 http://dx.doi.org/10.1098/rsos.201443 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Computer Science and Artificial Intelligence
Botta, Federico
Quantifying the differences in call detail records
title Quantifying the differences in call detail records
title_full Quantifying the differences in call detail records
title_fullStr Quantifying the differences in call detail records
title_full_unstemmed Quantifying the differences in call detail records
title_short Quantifying the differences in call detail records
title_sort quantifying the differences in call detail records
topic Computer Science and Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8242929/
https://www.ncbi.nlm.nih.gov/pubmed/34234948
http://dx.doi.org/10.1098/rsos.201443
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