SACM - United Kingdom

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AI-Driven Approaches for Privacy Compliance: Enhancing Adherence to Privacy Regulations
(Univeristy of Warwick, 2024-02) Alamri, Hamad; Maple, Carsten
This thesis investigates and explores some inherent limitations within the current privacy policy landscape, provides recommendations, and proposes potential solutions to address these issues. The first contribution of this thesis is a comprehensive study that addresses a significant gap in the literature. This study provides a detailed overview of the current landscape of privacy policies, covering both their limitations and proposed solutions, with the aim of identifying the most practical and applicable approaches for researchers in the field. Second, the thesis tackles the challenge of privacy policy accessibility in app stores by introducing the App Privacy Policy Extractor (APPE) system. The APPE pipeline consists of various components, each developed to perform a specific task and provide insightful information about the apps' privacy policies. By analysing over two million apps in the iOS App Store, APPE offers unprecedented and comprehensive store-wide insights into policy distribution and can act as a mechanism for enforcing privacy policy requirements in app stores automatically. Third, the thesis investigates the issue of privacy policy complexity. By establishing generalisability across app categories and drawing attention to associated matters of time and cost, the study demonstrates that the current situation requires immediate and effective solutions. It suggests several recommendations and potential solutions. Finally, to enhance user engagement with privacy policies, a novel framework utilising a cost-effective unsupervised approach, based on the latest AI innovations, has been developed. The comparison of the findings of this study with state-of-the-art methods suggests that this approach can produce outcomes that are on par with those of human experts, or even surpass them, yet in a more efficient and automated manner.
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Evaluating CAMeL-BERT for Sentiment Analysis of Customer Satisfaction with STC (Saudi Telecom Company) Services
(The University of Sussex, 2024-08-15) Alotaibi, Fahad; Pay, Jack
In the age of informatics platforms such as Twitter (X) plays a crucial role for measuring public sentiment, especially in both private and public sectors. This study explores the application of machine learning, particularly deep learning, to perform sentiment analysis on tweets about Saudi Telecom Company (STC) services in Saudi Arabia. A comparative analysis was conducted between pre-trained sentiment analysis models in English and in Arabic to assess their effectiveness in classifying sentiments. In addition, the study highlights a challenge in existing Arabic models, which are based on English model architectures but trained on varied datasets, such as Modern Standard Arabic and Classical Arabic (Al-Fus’ha). These models often lack the capability to handle the diverse Arabic dialects commonly used on social media. To overcome this issue, the study involved fine-tuning a pre-trained Arabic model using a dataset of tweets related to STC services, specifically focusing on the Saudi dialect. Data was collected from Twitter (X), focusing on mentions of the Saudi Telecom Company (STC). Both English and Arabic models were applied to this data, and their performance in sentiment analysis was evaluated. The fine-tuned Arabic model (CAMeL-BERT) demonstrated improved accuracy and a better understanding of local dialects compared to its initial version. The results highlight the importance of model adaptation for specific languages and contexts and underline the potential of CAMeL-BERT in sentiment analysis for Arabic-language content. The findings offer practical implications for enhancing customer service and engagement through more accurate sentiment analysis of social media content in the service providers sector.
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Unsupervised Semantic Change Detection in Arabic
(Queen Mary University of London, 2023-10-23) Sindi, Kenan; Dubossarsky, Haim
This study employs pretrained BERT models— AraBERT, CAMeLBERT (CA), and CAMeLBERT (MSA)—to investigate semantic change in Arabic across distinct time periods. Analyzing word embeddings and cosine distance scores reveals variations in capturing semantic shifts. The research highlights the significance of training data quality and diversity, while acknowledging limitations in data scope. The project's outcome—a list of most stable and changed words—contributes to Arabic NLP by shedding light on semantic change detection, suggesting potential model selection strategies and areas for future exploration.
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Hate Speech Detection for the Arabic Language
(Saudi Digital Library, 2023-11-03) Alhejaili, Abrar; Moosavi, Nafise
As online social networks grow and communication technologies become more available, people can exercise their freedom of expression more than ever before. Even though the interaction between users on these platforms can be constructive, they are increasingly used for spreading hateful content, mainly due to the anonymity feature of these online platforms. Hate speech can induce cyber conflict, negatively impacting social life at both the individual and national levels. In spite of this, social network providers are unable to monitor all the content posted by their users. As a result, there is a need to detect hate speech automatically. This need increases when the text is written in a language like Arabic. Arabic is known for its challenges, complexities, and resource scarcity. This project uses transfer learning methods to adapt, and evaluate some pretrained models to detect hate speech in Arabic. Many experiments were conducted in this project to assess the transferring of some options from BERT and Sequence-to-Sequence families (e.g., DehateBERT, MARBERT, T5, and Flan-T5), and the transferring of preprocessing functions from a pretrained model (AraBERT). Experiments show that transfer learning by finetuning monolingual models has promising results to a different extent. In addition, the additional preprocessing can affect the performance in a good way. Nevertheless, dealing with low-frequency labels independently, such as our dataset’s hate class, is still challenging. Warning: This paper may include instances of offensive language.
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