Riazi, Diana;
(2025)
Agent-based Models of Social Learning in Opinion Dynamics.
Doctoral thesis (Ph.D), UCL (University College London).
Preview |
Text
RIAZI_10205241_Thesis.pdf Download (1MB) | Preview |
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
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Agent-based Models of Social Learning in Opinion Dynamics |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | 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. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10205241 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
