This thesis presents SAESNEG, a System for the Automated Extraction of Social Network Event Groups; a pipeline for the aggregation of the personal social media footprint, and its partitioning into events, the event clustering problem. SAESNEG facilitates a reminiscence-friendly user experience, where the user is able to navigate their social media footprint. A range of socio-technical issues are explored: the challenges to reminiscence, lifelogging, ownership, and digital death. Whilst previous systems have focused on the organisation of a single type of data, such as photos or Tweets respectively; SAESNEG handles a variety of types of social network documents found in a typical footprint (e.g. photos, Tweets, check-ins), with a variety of image, text and other metadata di erently heterogeneous data; adapted to sparse, private events typical of the personal social media footprint. Phase A extracts information, focusing on natural language processing; new techniques are developed; including a novel distributed approach to handling temporal expressions, and a parser for social events (such as birthdays). Information is also extracted from image and metadata, the resultant annotations feeding the subsequent event clustering. Phase B performs event clustering through the application of a number of pairwise similarity strategies a mixture of new and existing algorithms. Clustering itself is achieved by combining machine-learning with correlation clustering. The main contributions of this thesis are the identi cation of the technical research task (and the associated social need), the development of novel algorithms and approaches, and the integration of these with existing algorithms to form the pipeline. Results demonstrate SAESNEG's capability to perform event clustering on a di erently heterogeneous dataset, enabling users to achieve lifelogging in the context of their existing social media networks.