Data analytics of call log data to identify caller behaviour patterns from a mental health and well-being helpline

Siobhan O’Neill, Raymond R. Bond, Alexander Grigorash*, Colette Ramsey, Cherie Armour, Maurice D. Mulvenna

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Number of pages17
JournalHealth Informatics Journal
Early online date17 Sep 2018
Publication statusEarly online date - 17 Sep 2018


  • clustering methods
  • Fourier series
  • Fourier transform
  • frequency estimation
  • healthcare service usage
  • help-seeking behaviour
  • machine learning
  • mental health
  • mental health and well-being helpline
  • psychology
  • suicide
  • telephony analysis
  • well-being

ASJC Scopus subject areas

  • Health Informatics


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