Lifelogging with SAESNEG: a system for the automated extraction of social network event groups

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Author
Blamey, Benjamin
Date
2015Type
Thesis
Publisher
Cardiff Metropolitan University
Metadata
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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.
Description
PhD Thesis- School of Management
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