An ontology-based framework for the modelling and online detection of Obsessive Compulsive Disorder

Muhajab, Areej Nasser 2024. An ontology-based framework for the modelling and online detection of Obsessive Compulsive Disorder. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

In the contemporary digital landscape, the prevalence and impact of Obsessive- Compulsive Disorder (OCD) discourse in online platforms have garnered increasing significance. This thesis presents an integrated framework aimed at detecting and classifying OCD in online discourse by harnessing the synergy between ontology development and machine learning. The primary objective is to enhance the understanding and identification of OCD-related content within the vast and varied landscape of online forums. The research begins with the construction of a comprehensive ontology, named OCD, specifically designed to encapsulate the multifaceted aspects of OCD. This ontology is developed to represent the complex interplay of OCD symptoms, behaviors, and related mental health concepts. Drawing upon insights from medical literature, psychological studies, and existing biomedical ontologies, the OCD ontology provides a structured, hierarchical representation of OCD, enabling systematic identification and categorisation of OCD-related terms. Consequently, it furnishes a rich semantic framework that facilitates accurate interpretation of online discourse. In addition to ontology development, the thesis explores machine learning methodologies, particularly focusing on the classification of OCD-related posts on online platform. A variety of classification models are employed to analyse and categorise online content. Leveraging the OCD ontology as a foundational reference for feature extraction and semantic analysis, these models are trained and evaluated on a corpus of OCD forum posts. The classification process is designed to discern various OCD manifestations, such as obsessions and compulsions, thereby offering a granular understanding of the disorder’s portrayal in digital communication. The outcomes of this thesis carry significant implications for mental health professionals, online community moderators, and researchers. The developed framework and methodologies represent a pioneering tool for monitoring, understanding, and addressing OCD in the digital space.

Item Type:	Thesis (PhD)
Date Type:	Acceptance
Status:	Unpublished
Schools:	Computer Science & Informatics
Date of First Compliant Deposit:	13 November 2024
Date of Acceptance:	November 2024
Last Modified:	15 Nov 2024 10:36
URI:	https://orca.cardiff.ac.uk/id/eprint/173927

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