Saudi Cultural Missions Theses & Dissertations
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Item Restricted Fake News Detection on Social Media: Methods and Techniques(University of Leeds, 2024) Althabiti, Saud; Alsalka, Mohammad Ammar; Atwell, EricThis thesis explores a number of new techniques for detecting fake news on social media and culminates with proposing a comprehensive fact-checking system. Although the main focus is on the Arabic language, the proposed methods and techniques are adaptable to other languages. The effectiveness of these methods has been evaluated through various experiments using data from social media platforms such as Twitter and other sources of potential misinformation. A literature review of studies in the field reveals a significant amount of research focused on applying AI methods to automate the detection of online fake news. However, current research in this direction has several weaknesses in various areas, including challenges in the entire detection pipeline, limitations in the employed methodologies, and inadequacies in existing datasets. The thesis begins by introducing a novel approach to simulating interactions on social networks, which enables the tracking of user behaviours and the propagation of news to assess credibility. It then examines four different techniques that can be considered for building an automatic fact-checking system and concludes with the proposal of a hybrid unified pipeline. The first technique focuses on classifying claims based solely on their content. To evaluate this approach, three studies were conducted using different methods. The proposed methods demonstrated promising results, which achieved a macro F-score of 0.339 in the third study. These findings suggest that content-based techniques can be improved by incorporating additional information. The second technique expands claim classification by incorporating both content and additional external information. New structured methodologies were developed to extract potential features rather than relying solely on claim content. Examples of such methodologies include identifying sarcastic or hateful comments. Although the results showed that these features did not improve classification performance, they highlighted the potential value of such indicators. Specifically, the findings revealed that sarcasm or hate speech is nearly twice as prevalent in comments on false claims compared to true ones. The third technique aims to automate fact-checking explainability based on the content of claims and news articles. To support this approach, a new dataset, FactEx, was collected from trusted fact-checking systems. This dataset was used to fine-tune generative models, and among these, the best-performing model achieved a ROUGE score of 23.4. The fourth technique involves fact-checking claims through retrieved information. A new verification system, named Ta’keed, was developed based on this technique to fact-check Arabic claims. Additionally, a new gold-labelled test set, ArFactEx, was compiled to assess Ta’keed. An evaluation investigation reveals that the proposed system exceeded models such as AraBERT when fine-tuned on three benchmark Arabic datasets and tested on ArFactEx. It achieved an F1-score of 0.72 compared to 0.52, 0.61, and 0.54 by the other fine-tuned models. It also outperformed T5-based and AraT5-based models in generating justifications, with an average cosine similarity score of 0.76. Finally, an optimised hybrid pipeline was introduced, which incorporates information retrieval and evidence extraction to enhance the classification task. The final proposed pipeline achieved an F1-score of 0.86, which highlighted the importance of information retrieval in tackling disinformation.32 0Item Restricted Generative AI Technologies Use Among Higher Education Students in Saudi Arabia: Benefits and Concerns(University of Southampton, 2024) AlKhunayfir, Sarah; Zarifis, AlexThis study investigates the use of generative AI technologies among higher education students in Saudi Arabia, focusing on perceived benefits and concerns. As these technologies rapidly integrate into academic environments, understanding their impact becomes crucial for effective implementation and policy development. The research aims to identify specific benefits in terms of time savings, unique insights, and personalised feedback, while also examining concerns regarding overreliance, data privacy, and information accuracy. Employing a quantitative approach, the study utilised a closed-questions survey distributed to 150 higher education students in Saudi Arabia. The survey gathered data on students' perceptions and usage patterns of generative AI technologies, which were then analysed using descriptive and inferential statistical methods. Findings reveal a nuanced landscape of student attitudes. Students perceive significant benefits from generative AI, with time savings emerging as the most appreciated advantage, followed by gaining unique insights and receiving personalised feedback. Concurrently, moderate levels of concern were identified, primarily regarding the accuracy of AI-generated content and potential overreliance on these technologies. Interestingly, data privacy concerns were less pronounced than anticipated. The study concludes that while students recognise the transformative potential of generative AI in enhancing learning experiences, they remain cautious about its limitations. These findings contribute to the understanding of AI integration in Saudi higher education and offer valuable insights for developing balanced, ethical, and effective AI integration strategies. The research underscores the need for ongoing dialogue, policy development, and further investigation to ensure that the integration of generative AI aligns with educational goals and societal values in Saudi Arabia.22 0Item Restricted Developing a Generative AI Model to Enhance Sentiment Analysis for the Saudi Dialect(Texas Tech University, 2024-12) Aftan, Sulaiman; Zhuang, YuSentiment Analysis (SA) is a fundamental task in Natural Language Processing (NLP) with broad applications across various real-world domains. While Arabic is a globally significant language with several well-developed NLP models for its standard form, achieving high performance in sentiment analysis for the Saudi Dialect (SD) remains challenging. A key factor contributing to this difficulty is inadequate SD datasets for training of NLP models. This study introduces a novel method for adapting a high-resource language model to a closely related but low-resource dialect by combining moderate effort in SD data collection with generative AI to address this problem of inadequacy in SD datasets. Then, AraBERT was fine-tuned using a combination of collected SD data and additional SD data generated by GPT. The results demonstrate a significant improvement in SD sentiment analysis performance compared to the AraBERT model, which is fine-tuned with only collected SD datasets. This approach highlights an efficient approach to generating high-quality datasets for fine-tuning a model trained on a high-resource language to perform well in a low-resource dialect. Leveraging generative AI enables reduced effort in data collection, making our approach a promising avenue for future research in low-resource NLP tasks.37 0Item Restricted Navigating Arabic Sentiments: An Evaluation of Multilingual and Arabic Dedicated Large Language Models(University of Exeter, 2024) Altowairqi, Hadeel; Menezes, RonaldoExpressing emotions in written text, especially in Arabic with its complex structure and poetic elements, can be challenging.While body language enriches spoken communication with emotional depth, written Arabic often lacks this nuance. The advent of Large Language Models (LLMs) has revolutionized natural language processing (NLP), excelling in tasks like text generation and sentiment analysis. However, the performance of these models varies significantly depending on the language and task. Arabic poses unique challenges due to its complex morphology and diverse dialects. This research investigates the impact of LLMs, particularly those tailored for Arabic, on the emotional depth of the written text. By evaluating how these models modify expressions, the study aims to understand whether LLMs preserve or constrain the intricate emotional nuances inherent in Arabic. The findings will contribute to the development of more effective AI tools for digital communication in the Arabic-speaking world, enhancing applications in fields such as sentiment analysis, opinion mining, and content moderation. Through a comprehensive analysis of over 81,000 Arabic texts, including tweets and book reviews, the study examines the performance of the general-purpose LLM ChatGPT and the Arabic-specific LLM JAIS, focusing on the sentiment shifts introduced by their edits. The results reveal a significant tendency of these models to introduce a positive bias, reducing the frequency of extremely negative sentiments. These insights highlight the necessity of incorporating cultural and linguistic nuances into LLM training data, emphasizing the importance of responsible development and ethical considerations in LLM applications.16 0