Advancing Cross-Domain Fake News Detection: Enhanced Models to Improve Generalization and Tackle the Class Imbalance Problem
type: CreativeWork
description: The rapid proliferation of fake news across domains, such as politics, health, and social media, poses a significant threat to the integrity of information dissemination, leading to misinformation that can affect public perception and decision-making. Detecting fake news is critical to preserving the credibility of media sources, protecting democratic processes, and ensuring public safety. However, as Artificial Intelligence (AI)-driven Fake News Detection (FND) systems become more prevalent, ensuring AI safety is equally important to prevent unintended biases, ethical concerns, and adversarial exploitation of these models. Several challenges hinder the effectiveness of current FND models. Among these, cross-domain generalization and class imbalance are two critical problems that considerably impact the performance of detection systems. Although there are multiple challenges in FND, this thesis focuses on these two problems due to their widespread influence across various datasets and domains. Cross-domain generalization is a complex problem because the characteristics of fake news differ significantly between domains. For example, fake news in politics may be presented in a completely different manner when compared to health-related misinformation. This variation in linguistic and contextual features makes it difficult for models trained in one domain to generalize to another. Still, important patterns can be present across domains and might be useful to detect fake news on different domains, helping with model generalizability. This thesis adopts a domain classification approach to address this issue, allowing the model to classify the domain before applying a domain-specific FND strategy, thereby improving overall generalization. The class imbalance, where fake news is underrepresented in datasets compared to real news, presents another challenge, as models often become biased toward the majority class. This thesis addresses this problem by incorporating advanced…
abstract: The rapid proliferation of fake news across domains, such as politics, health, and social media, poses a significant threat to the integrity of information dissemination, leading to misinformation that can affect public perception and decision-making. Detecting fake news is critical to preserving the credibility of media sources, protecting democratic processes, and ensuring public safety. However, as Artificial Intelligence (AI)-driven Fake News Detection (FND) systems become more prevalent, ensuring AI safety is equally important to prevent unintended biases, ethical concerns, and adversarial exploitation of these models. Several challenges hinder the effectiveness of current FND models. Among these, cross-domain generalization and class imbalance are two critical problems that considerably impact the performance of detection systems. Although there are multiple challenges in FND, this thesis focuses on these two problems due to their widespread influence across various datasets and domains. Cross-domain generalization is a complex problem because the characteristics of fake news differ significantly between domains. For example, fake news in politics may be presented in a completely different manner when compared to health-related misinformation. This variation in linguistic and contextual features makes it difficult for models trained in one domain to generalize to another. Still, important patterns can be present across domains and might be useful to detect fake news on different domains, helping with model generalizability. This thesis adopts a domain classification approach to address this issue, allowing the model to classify the domain before applying a domain-specific FND strategy, thereby improving overall generalization. The class imbalance, where fake news is underrepresented in datasets compared to real news, presents another challenge, as models often become biased toward the majority class. This thesis addresses this problem by incorporating advanced…
author: Alnabhan, Mohammad Qasim Mohammad
language: en
keywords: Large Language Model, Deep Learning, Transfer Learning, Class Imbalance, Applied Artificial Intelligence (AI), AI Safety, Model Generalizability
date published: 2025-03-12T17:56:40Z
date modified: 2026-07-24T03:39:38Z
provider: University of Ottawa
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Université d'Ottawa / University of Ottawa

PropertyValue
property ID: Handle
value: http://hdl.handle.net/10393/50256

PropertyValue
property ID: DOI
value: 10.20381/ruor-30972