  This thesis develops a conceptual framework considering social data as
representing the surface layer of a hierarchy of human social behaviours, needs
and cognition which is employed to transform social data into representations
that preserve social behaviours and their causalities. Based on this framework
two platforms were built to capture insights from fast-paced and slow-paced
social data. For fast-paced, a self-structuring and incremental learning
technique was developed to automatically capture salient topics and
corresponding dynamics over time. An event detection technique was developed to
automatically monitor those identified topic pathways for significant
fluctuations in social behaviours using multiple indicators such as volume and
sentiment. This platform is demonstrated using two large datasets with over 1
million tweets. The separated topic pathways were representative of the key
topics of each entity and coherent against topic coherence measures. Identified
events were validated against contemporary events reported in news. Secondly
for the slow-paced social data, a suite of new machine learning and natural
language processing techniques were developed to automatically capture
self-disclosed information of the individuals such as demographics, emotions
and timeline of personal events. This platform was trialled on a large text
corpus of over 4 million posts collected from online support groups. This was
further extended to transform prostate cancer related online support group
discussions into a multidimensional representation and investigated the
self-disclosed quality of life of patients (and partners) against time,
demographics and clinical factors. The capabilities of this extended platform
have been demonstrated using a text corpus collected from 10 prostate cancer
online support groups comprising of 609,960 prostate cancer discussions and
22,233 patients.
