  Over the past 15 years, the volume, richness and quality of data collected
from the combined social networking platforms has increased beyond all
expectation, providing researchers from a variety of disciplines to use it in
their research. Perhaps more impactfully, it has provided the foundation for a
range of new products and services, transforming industries such as advertising
and marketing, as well as bringing the challenges of sharing personal data into
the public consciousness. But how to make sense of the ever-increasing volume
of big social data so that we can better understand and improve the user
experience in increasingly complex, data-driven digital systems. This link with
usability and the user experience of data-driven system bridges into the wider
field of HCI, attracting interdisciplinary researchers as we see the demand for
consumer technologies, software and systems, as well as the integration of
social networks into our everyday lives. The fact that the data largely posted
on social networks tends to be textual, provides a further link to linguistics,
psychology and psycholinguistics to better understand the relationship between
human behaviours offline and online.
  In this thesis, we present a novel conceptual framework based on a complex
digital system using collected longitudinal datasets to predict system status
based on the personality traits and emotions extracted from text posted by
users. The system framework was built using a dataset collected from an online
scholarship system in which 2000 students had their digital behaviour and
social network behaviour collected for this study. We contextualise this
research project with a wider review and critical analysis of the current
psycholinguistics, artificial intelligence and human-computer interaction
literature, which reveals a gap of mapping and understanding digital profiling
against system status.
