On Measuring Social Dynamics of Online Social Media
Date
2016
Authors
Wood, Ian David
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Abstract
Due to the complex nature of human behaviour and to our inability
to directly measure thoughts and feelings, social psychology has
long struggled for empirical grounding for its theories and
models. Traditional techniques involving groups of people in
controlled environments are limited to small numbers and may not
be a good analogue for real social interactions in natural
settings due to their controlled and artificial nature. Their
application as a foundation for simulation of social processes
suffers similarly.
The proliferation of online social media offers new opportunities
to observe social phenomena “in the wild” that have only just
begun to be realised. To date, analysis of social media data has
been largely focussed on specific, commercially relevant goals
(such as sentiment analysis) that are of limited use to social
psychology, and the dynamics critical to an understanding of
social processes is rarely addressed or even present in collected
data.
This thesis addresses such shortfalls by: (i) presenting a novel
data collection strategy and system for rich dynamic data from
communities operating on Twitter; (ii) a data set encompassing
longitudinal dynamic information over two and a half years from
the online pro-ana (pro-anorexia) movement; and (iii) two
approaches to identifying active social psychological processes
in collections of online text and network metadata: an approach
linking traditional psychometric studies with topic models and an
algorithm combining community detection in user networks with
topic models of the social media text they generate, enabling
identification of community specific topic usage.
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Keywords
social dynamics, social media, Twitter, topic models, community detection, network analysis, social psychology, empirical psychology, social simulation, social representation theory
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Thesis (PhD)
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