@article{ref1,
title="Data analytics of call log data to identify caller behaviour patterns from a mental health and well-being helpline",
journal="Health informatics journal",
year="2018",
author="O'Neill, Siobhan and Bond, Raymond R. and Grigorash, Alexander and Ramsey, Colette and Armour, Cherie and Mulvenna, Maurice D.",
volume="ePub",
number="ePub",
pages="1460458218792668-1460458218792668",
abstract="This work presents an analysis of 3.5 million calls made to a mental health and well-being helpline, seeking to answer the question, what different groups of callers can be characterised by specific usage patterns? Calls were extracted from a telephony informatics system. Each call was logged with a date, time, duration and a unique identifier allowing for repeat caller analysis. We utilized data mining techniques to reveal new insights into help-seeking behaviours. Analysis was carried out using unsupervised machine learning (K-means clustering) to discover the types of callers, and Fourier transform was used to ascertain periodicity in calls. Callers can be clustered into five or six caller groups that offer a meaningful interpretation. Cluster groups are stable and re-emerge regardless of which year is considered. The volume of calls exhibits strong repetitive intra-day and intra-week patterns. Intra-month repetitions are absent. This work provides new data-driven findings to model the type and behaviour of callers seeking mental health support. It offers insights for computer-mediated and telephony-based helpline management.<p /> <p>Language: en</p>",
language="en",
issn="1460-4582",
doi="10.1177/1460458218792668",
url="http://dx.doi.org/10.1177/1460458218792668"
}