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

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posted on 2022-10-24, 14:48 authored by Benjamin Blamey


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


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 differently

heterogeneous dataset, enabling users to achieve lifelogging in the context of

their existing social media networks.



  • School of Management

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  • Doctoral

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  • PhD

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