eprintid: 10205241
rev_number: 12
eprint_status: archive
userid: 699
dir: disk0/10/20/52/41
datestamp: 2025-03-13 07:54:09
lastmod: 2025-03-13 07:54:09
status_changed: 2025-03-13 07:54:09
type: thesis
metadata_visibility: show
sword_depositor: 699
creators_name: Riazi, Diana
title: Agent-based Models of Social Learning in Opinion Dynamics
ispublished: unpub
divisions: UCL
divisions: B04
divisions: F48
note: 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.
abstract: 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
date: 2025-02-28
date_type: published
oa_status: green
full_text_type: other
thesis_class: doctoral_open
thesis_award: Ph.D
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2364321
lyricists_name: Riazi, Diana
lyricists_id: DRIAZ72
actors_name: Riazi, Diana
actors_id: DRIAZ72
actors_role: owner
full_text_status: public
pages: 125
institution: UCL (University College London)
department: Computer Science
thesis_type: Doctoral
citation:        Riazi, Diana;      (2025)    Agent-based Models of Social Learning in Opinion Dynamics.                   Doctoral thesis  (Ph.D), UCL (University College London).     Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10205241/1/RIAZI_10205241_Thesis.pdf