TY  - UNPB
A1  - Riazi, Diana
M1  - Doctoral
UR  - https://discovery.ucl.ac.uk/id/eprint/10205241/
PB  - UCL (University College London)
N2  - With phenomena such as the rise of fake news and the spread of online misinformation, the need to develop a quantitative understanding of opinion dynamics in online social networks (OSNs) has become evident. By gauging agent-based models
(ABMs), this thesis aims to better understand the relationship which exists between
mis/dis-information spread and opinion formation, in the interest of determining the
effectiveness of current counter-strategies and ultimately striving towards improved
ones.
A social learning procedure of distributed hypothesis testing (DHT) is invoked
in order to build a framework where there exists a notion of ground-truth. Agents
aim to learn this ground-truth among a set of competing hypotheses utilizing this
procedure of DHT. A sub-population of agents, referred to as conspirators which
promote a fixed non-truth, are introduced where the dynamics are observed given
a level of the network being perturbed. To understand such effects, a metric of
truthfulness is developed, which describes the network?s collective belief in the
ground-truth.
A feature of the DHT procedure invoked is the distinction between what an
agent publicly expresses and privately believes. This distinction gives rise to a
means of measuring an agent?s cognitive dissonance, evocative of the mental toll
which may arise when a decision is faced within an environment of potentially
conflicting information.
A main prong of this thesis is to understand whether the effects of misinformation may be mitigated. Given our DHT framework, it is explored whether there
are beneficial effects of the injection of noise, which has been shown to have stabilizing effects in complex system contexts. Finally, we investigate by whom or
what the moderation of misinformation should derive. We classify current counterstrategies (debunking, prebunking, deplatforming, and inoculation) as centralized
and/or decentralized and simulate such correcting interventions
ID  - discovery10205241
N1  - Copyright © The Author 2025. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author?s request.
AV  - public
Y1  - 2025/02/28/
EP  - 125
TI  - Agent-based Models of Social Learning in Opinion Dynamics
ER  -