Monitoring communications: aiming to identify periods of unusually increased communications between parties of interest

Abstract

Monitoring to detect changes in the communication levels within a social network is a new area of research. There is little available on this topic in the current literature. This paper proposes that existing spatio-temporal surveillance technology could be used as a starting point for monitoring these changes. A number of challenges were encountered. The first involved the ordering of individuals into ‘neighbours’ so that the spatio-temporal surveillance technology and approaches could be applied. The second difficulty encountered was the computational effort involved in monitoring large numbers of individuals. In order to address this computational issue, cell communication level aggregation based on the order statistics of standardized cell communication count departures from expected was attempted. Simulations were used to compare two computationally feasible options.

Keywords:

• Average run length
• crime
• false discovery
• scan statistic
• social media
• statistical process control
• surveillance

Acknowledgements

The author would like to thank Maree O’Sullivan and Rob McGregor for their comments on this paper which has helped improve the paper.

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Notes on contributor

Ross Sparks, is a Statistician with over 33 years research and teaching experience at universities and with research based consulting at CSIRO, Australia. His role is in leading strategic and tactical research projects in CSIRO Computational Informatics in the area of multivariate spatio-temporal monitoring and spatio-temporal modelling. He has published 60 papers in refereed journals, nine book chapters, 15 papers in conference proceedings and 10 articles in trade magazines. He has made a number of research contributions in the areas of (multivariate) process monitoring, spatio-temporal modelling and the handling of (partially) missing data. Specifically, he has contributed to topics in the variable selection in multivariate regression models, outlier detection in regression models, model validation and assessment, quality control and assurance, cluster analysis, dimension reduction methods and disease surveillance.