Channel-Specific Daily Patterns in Mobile Phone Communication
July 16, 2015 Β· Declared Dead Β· π European Conference on Complex Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Talayeh Aledavood, Eduardo LΓ³pez, Sam G. B. Roberts, Felix Reed-Tsochas, Esteban Moro, Robin I. M. Dunbar, Jari SaramΓ€ki
arXiv ID
1507.04596
Category
physics.soc-ph
Cross-listed
cs.SI
Citations
32
Venue
European Conference on Complex Systems
Last Checked
3 months ago
Abstract
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 that 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 vs. 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 communication channels and gender differences.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β physics.soc-ph
π
π
The Cartographer
R.I.P.
π»
Ghosted
Networks beyond pairwise interactions: structure and dynamics
R.I.P.
π»
Ghosted
Statistical physics of human cooperation
R.I.P.
π»
Ghosted
Vital nodes identification in complex networks
R.I.P.
π»
Ghosted
Influence maximization in complex networks through optimal percolation
R.I.P.
π»
Ghosted
Scale-free networks are rare
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted