Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted Beyond Likes and Shares: How Humours’ brand engagement strategies in comment sections strengthen consumer-brand relationships(Saudi Digital Library, 2025) Alsubhi, Lubna; Duncan-Shepherd, SophieSocial media channels have evolved into powerful marketing tools, driving success for both business and non-profit organizations. As social media has evolved, it's empowered consumers in digital spaces to have a strong and active voice when interacting with various brands and organizations. This research delves into how brands that use humour, playfulness, and positivity in social media comment sections affect consumer perception, loyalty, and purchasing intentions. The data collection was gathered through a quantitative survey from 105 respondents. The respondents were social media users across various social media platforms. The findings show that casual and humorous brand communication has a significant impact on consumer responses, accounting for 36.4% of the variance in how consumers perceive brands. Entertainment and humorous content were the main drivers of engagement, outperforming educational content and sales promotions. The study showed that brands that interact playfully can greatly improve how consumers see them and how likely they are to buy, with responsive brands seen as part of consumers' social circle, not just a commercial entity.4 0Item Restricted Leveraging Social Media Data for Detection and Monitoring of Depression(Saudi Digital Library, 2025) Alhamed, Falwah Abdulaziz; Specia, Lucia; Specia, LuciaMental health disorders are increasingly prevalent, with depression being the most common and a significant cause of disability and suicide worldwide. Understanding its symptoms, severity, and progression is vital for improving early detection and intervention. This thesis adopts a data-driven AI approach, constructing a large, expert-annotated dataset and developing models to monitor depression from social media language. We first design a data collection and curation framework to build a large-scale dataset of posts from individuals who self-report depression. In collaboration with psychiatrists and psychologists, we create an annotation scheme for labelling symptoms and severity over time. Experienced psychologists annotate the data, resulting in DepSy, the largest English dataset of 40,000 posts fully annotated for depression symptoms and severity progression. This dataset underpins all subsequent experiments. We then benchmark multiple NLP approaches to classify posts written before versus after a reported depression diagnosis. Analyses include linguistic patterns, emotion usage, and content variation. Among the models tested, BERT-based classifiers achieve the best overall performance, while large language models (LLMs) in zero-shot settings perform near-randomly. Next, we address symptom detection as a multi-label classification problem. A bespoke BERT-based model achieves strong overall results, while a fine-tuned Llama-based model, DepSy-LLaMA, obtains higher recall, identifying more positive symptom cases—a valuable property in mental health detection. However, LLM predictions remain less reliable for sensitive applications. Finally, we explore the prediction of depression severity over time using deep learning and propose a hybrid CTMC-LSTM model that integrates Markov chains with LSTM to capture temporal patterns. This model uniquely detects severe cases and achieves the highest performance across all baselines. The findings demonstrate the importance of temporal modelling and expert-annotated data for building robust, ethical, and clinically informed systems for depression monitoring from social media.12 0Item Restricted Optimizing Hate Text Detection using Custom NLP Techniques and an Adapted DeBERTa-based Machine Learning Model(Saudi Digital Library, 2025) Aljabbar, Abdullah; AlYamani, AbdulghaniThe rapid expansion of social media has transformed online communication, providing platforms for public debate and community engagement. However, this openness has also facilitated the spread of harmful content, particularly hate speech, which poses significant risks to individual well-being, social cohesion, and digital trust. Detecting such content remains a major challenge due to the subtle, context-dependent, and evolving nature of hateful expressions. Traditional machine learning models, though useful as early baselines, often fail to capture linguistic nuance and contextual depth. Recent advances in natural language processing (NLP), particularly Transformer-based architectures, have significantly improved text classification tasks by enabling context-sensitive embeddings. This research investigates the effectiveness of DeBERTa (Decoding-enhanced BERT with Disentangled Attention) for hate speech detection. The study employs a systematic methodology consisting of four stages: data preparation and preprocessing, exploratory data analysis, model development, and evaluation. A curated dataset of 2,041 social media posts, derived from a larger corpus, was pre-processed to remove noise, normalise text, and correct class imbalance. The DeBERTa-v3-large model was fine-tuned using cross-entropy loss and AdamW optimisation. Performance was assessed with accuracy, precision, recall, F1-score, ROC, and PR curves, while error analysis and confusion matrices were used to identify common misclassifications. The findings demonstrate that DeBERTa can effectively capture indirect meaning and grammar connections. Additionally, outperforming traditional approaches and offering robust classification of hate and non- hate content. The study contributes to both NLP research and the wider cybersecurity domain by supporting the development of more reliable automated moderation tools that promote safer digital environments.14 0Item Restricted Enhancing Cross-lingual Transfer Learning for Crisis Text Classification on Social Media(Saudi Digital Library, 2025) AlAmer, Shareefa; Lee, Mark; Smith, PhillipDuring crisis events such as natural disasters, conflicts, and pandemics, social media platforms serve as vital channels for real-time information sharing. These platforms enable users to post urgent updates, request assistance, and disseminate situational awareness at a scale and speed that traditional communication systems cannot match. Automatically classifying user-generated content in these contexts is essential for supporting timely emergency response. However, performing this task across multiple languages remains a major challenge, especially given that such content is often noisy, informal, and linguistically diverse. One of the core challenges lies in the scarcity of annotated data that is both domain- and task-specific. Even widely spoken languages may lack labelled resources tailored to specific applications, effectively rendering them low-resource for those tasks. Existing solutions for cross-lingual transfer remain suboptimal even when applied to more structured and formal data using complex architectures, which further highlights their limitations when handling the noisier and less predictable nature of social media content. In response to these challenges, this thesis investigates practical and scalable solutions to improve the cross-lingual classification of crisis-related social media content. The research explores four key directions: (1) evaluating Machine Translation as a strategy for augmenting training data in low-resource languages; (3) applying ensemble learning to enhance robustness across multilingual inputs; (3) examining data balancing methods to mitigate class imbalance; and (4) analysing interlingual transfer dynamics to identify how languages interact in multilingual learning setups. The evaluation of the proposed approach is performed through extensive experimentation on a real-world dataset of crisis-related X-posts (formerly known as tweets). The proposed methods achieve competitive results despite challenges posed by noisy social media text, class imbalance, and the lack of annotated data. This work presents a generalisable framework for multilingual crisis classification and offers insights that are valuable for real-world applications where language diversity and data scarcity are critical factors. The effectiveness of the proposed system can be further enriched by incorporating a wider range of languages and leveraging more advanced analytical models. Additionally, adopting advanced translation techniques could also be explored for even greater impact in future crisis response systems.27 0Item Restricted The role of social media in cultural awareness among Saudi University students(Saudi Digital Library, 2025) Alqarni, Muhsen Mudhhi; Bañon, CastellónThis era is characterised by rapid development in technology and communication, which has led to many rapid changes locally and globally. This study investigates the role of social media in shaping cultural awareness among university students in Saudi Arabia. The investigation explores the interferences and opportunities of social media in cultural and social development in Saudi Arabia. The research uses mixed methods to analyse the responses of QRR Saudi university students and ST interviews conducted with university professors. It found that social media dramatically enhances students' cultural interaction and educational engagement. It increases the level of harmony in society and facilitates communication with specialists and official cultural accounts. The work addressed individual, political, economic, and cultural interferences on social media, which affect cultural awareness. It recommends combating information distortion and extremism. Moreover, activating the role of educational institutions, improving systems to monitor misleading content, and increasing diverse cultural content could lead to critical use and cooperation with others to promote diversity, exchange, and meaningful dialogue.14 0Item Restricted AI Impersonation on social media Analysing Human Characteristics and Ethical Implications(Saudi Digital Library, 2025) Almuammar, Eyad; Fahad, AhmadThis study explores the behavioural, ethical, social, and regulatory implications of AI bots that impersonate humans on social media platforms. As artificial intelligence becomes increasingly integrated into online communication, AI-driven bots are being deployed to mimic human users, influence opinions, and automate engagement. While these technologies offer efficiency, they also raise serious concerns about misinformation, manipulation, transparency, and digital trust. Using a structured online questionnaire distributed via platforms such as Twitter (X), LinkedIn, and WhatsApp, this research gathered responses from 57 participants. The survey examined user perceptions across multiple dimensions, including their confidence in identifying bots, behavioural changes due to bot exposure, ethical concerns, perceived political influence, and expectations for regulation and education. Findings indicate that while many users feel moderately confident in recognizing bots, they also express reduced trust and engagement when bots are suspected. Ethical concerns particularly around privacy and undisclosed AI interaction were prominent, and users widely supported stronger regulation, transparency tools, and public education initiatives. The study concludes that AI bots pose a significant challenge to online authenticity and democratic discourse and highlights the need for multi-stakeholder governance to ensure safe and ethical deployment of such technologies.30 0Item Restricted Exploring How Social Media Advertising Shape Cultural Saudi Identity in Saudi Arabia(University of Sheffield, 2024-08-29) Alaydaa, Sarah; Ogunmuyiwa, HakeemInvestigating how social media advertising shapes Saudi cultural identity is the goal of this study. The research reviewed the previous studies that conducted in the last decade to derive its results and conclusions. Social media platforms have fundamentally changed how people interact and communicate. Thus it's important to understand how these virtual social phenomena are affecting our feelings and ideas about ourselves and each other. Finding out how social media advertising affects cultural identity in a good and bad way is the goal of the research. The benefits can be seen in the freedom to engage with others, pick up new information, and deal with the outside world, as well as in the education of other people about their cultures. The negative consequences could manifest as a person adopting Western values, breaking free from the dominant social norms, or acquiring values and customs that are incompatible with Arab culture. Thus, the researcher thinks that the social media has positive effects on cultural identity and on society.30 0Item Restricted The evolution of Convenience and the Transition from Traditional to Online Consumer Experiences : Exploring Perceptions in the Synergy Between Strategic Branding, Social Media, and Online Purchases of Luxury Dessert Products(University of Strathclyde Business School, 2024) Alhalwani, Abdelrahman; Pyper, KeithThis MSc research aims to explore the interaction between key identified elements of strategic branding, social media engagement, and online communications within the luxury dessert market, evaluating consumer behavior and perceptions. The study aims to achieve the following primary objectives: 1. To examine the impact of strategic branding strategies employed by luxury dessert brands on consumer preferences. 2. To analyze the role of social media platforms in shaping consumer perceptions related to luxury dessert products. 3. To investigate the influence of product presentation on consumer behavior among consumers in the luxury dessert sector. 4. To examine how communications in branding and social media influence customer perceptions The results were then categorized into the following two main themes: 1. Impact of Strategic Branding and Packaging on Consumer Preferences 2. Role of Social Media in Shaping Consumer Perceptions & Behavior The context and background of this dissertation are to assist future entrepreneurs in this niche sector seeking to establish a presence. The luxury dessert market can be distinguished from non-luxury brands due to its focus on exclusivity, quality, sophisticated presentation, and perhaps artificial scarcity. Thus, the research guides future luxury brands to use branding strategies and social media to appeal to customers, while also observing how product presentations affect purchasing decisions. Readers should concentrate on how luxury brands leverage strategic branding elements i.e. quality, reputation, etc. The reader should focus on how content that draws an emotional response from them (whether positive or negative); and examine how social media strategies can enhance or undermine a brand's image. Finally, the focus should be shifted toward how luxury brands utilize packaging and design elements to create a sense of value that influences purchasing behavior. This research utilized a qualitative research method using semi-structured interviews as a data collection method. The results were then assessed using the thematic analysis framework. Strategic branding heavily influences consumer preferences, and brands that highlight quality assurance along with a strong brand reputation were preferred. Another interesting fact was that familiarity with the brand also plays a pivotal role in consumer choice. Results also indicated that social media presence and the type of content published heavily shape perceptions. Participants noted that engaging and conscious messaging was effective despite the noticeable skepticism toward influencer endorsements. Brands that maintain consistency and transparency tend to build more trust with consumers. Conversely, a correlation can be drawn that since many participants were less engaged with social media, perhaps traditional advertising may still hold a significant value.17 0Item Restricted The Influence of Social Media on Travel Destination Choices Among Millennials and Generation Z in Saudi Arabia(Bournemouth University, 2024) Shaibi, Abdulkarim; Ladkin, AdeleThis dissertation investigates the influence of social media on travel destination choices among Millennials and Generation Z in Saudi Arabia, a rapidly evolving digital landscape. The research aims to elucidate how different social media platforms—namely TikTok, Snapchat, and Instagram—affect these generations' travel preferences and explore the impact of various types of social media content, including videos, reviews, and user-generated content. Additionally, the study examines the role of demographic and psychographic factors, such as income level, education, and frequency of social media use, in determining reliance on these platforms for travel inspiration. A quantitative research approach was employed, utilising a structured online survey administered to a representative sample of Saudi Millennials and Generation Z. Data analysis was conducted through descriptive statistics and inferential methods, including Chi-Square tests and multiple regression analysis, to test the research hypotheses. The findings demonstrate that visual-centric platforms like TikTok, Snapchat, and Instagram significantly shape travel destination choices, with videos emerging as the most influential content type. The analysis also reveals that the frequency of social media use is a critical factor in the reliance on these platforms for travel planning, while other demographic variables have a lesser impact. Perceptions of credibility and trustworthiness of social media content are consistent across both generations. In light of these insights, the study recommends that tourism marketers prioritise high-quality visual content, leverage user-generated reviews, and employ personalised marketing strategies tailored to the interests of these digital-native cohorts. The dissertation concludes with recommendations for future research, including the need for longitudinal studies and cross-cultural comparisons.76 0Item Restricted The Use of Social Media for Non-Profit Organisations to Meet Social Goals; An international comparison between Saudi Arabia and the United Kingdom(University of Reading, 2024-10) Basnawi, Abdullah; Molesworth, Mike; Zhang, RubyThis thesis explores and examines the impact of using social media for non-profit organizations (NPOs) to meet the social goals of their communities in relation to Saudi Arabia and the United Kingdom. The current study aims to identify the best practices for using social media as a tool to attract clients and donors, who are the main resources for NPOs in both Saudi Arabia and the UK. Additionally, it seeks to indirectly measure the level of competence of Saudi NPOs in social media marketing. This study, which employs an interpretivist inductive approach and a qualitative method, aims to gain a deeper understanding of the use of social media by non-profit organizations. The study conducted 52 interviews with ten non-profit organizations in both Saudi Arabia and the UK, using a case study approach to understand their use of social media for marketing purposes. The study found that the use of social media differs from one association to another in terms of activity, management, material capacity, and the human element controlling the content published on social media. In addition, each association has its own problems that hinder the optimal use of these auxiliary tools. As part of their strategic plans, social media has become a modern and important way to attract the attention of donors and customers. Collaborating with influential individuals is a key strategy for reaching the largest possible audience. The study also found that social media can help people connect, learn, and help each other, especially during crisis times like COVID-19.29 0
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