Across much of the western world, political polarisation is on the rise. This has the effect
of hindering political discourse, stifling open discussion, and in extreme cases has led to
violence. The process of polarising and radicalising vulnerable individuals has migrated to
social media websites, which have been implicated in several high profile terror attacks.
Within this thesis we model and investigate various algorithms to prevent the spread
of polarisation and extremist ideology by employing agent-based modelling techniques
from the field of opinion dynamics. The contributions of our work include the following
aspects.
Firstly, we have developed a unified framework for opinion dynamics, allowing us
to experiment easily on a number of different existing models and bringing together
sometimes disparate innovations from across the field into one system.
Secondly, this unified framework has been implemented in a modular simulator able
to perfectly replicate results from purpose-built, stand-alone simulators for two widely
used models, namely Relative Agreement and CODA, and then released to the public as
the first general-purpose opinion dynamics simulator.
Thirdly, we have developed two new intervention algorithms, along with a new metric
for measuring the effectiveness of an intervention strategy, which aim to reduce the
spread of polarisation across a network with low computational cost. These methods are
compared to existing centrality-based methods upon a random network. The experimental
results show our proposed approaches outperform centrality measures. We find that our
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algorithms are able to prevent up to 40% of non-extremist agents becoming extreme by
removing only 10% of the network’s edges.
Fourthly, we have investigated the efficacy of these intervention algorithms on polarisation under different scenarios (e.g. variable costs, different network structures).
The experimental validation proves the proposed approach is robust and has performed
favourably compared existing methods such as centrality-based methods especially on
the second type of network.
Finally, we have developed a broadcast-based communication system for agents,
designed to mimic the one-way broadcast nature of a public social media post such as
Twitter, in contrast to the existing model which emulates a two-way private conversation. The experimental result shows a lessening of the impact of our interventions,
demonstrating the need for further investigation of such communication methods