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 Health Communication Administration in Health Crises in Saudi Arabia (The Case of COVID, 1999-2021)(Saudi Digital Library, 2026) Alshammari, Mohammad Ayed M; Juan, Salvador VictoriaThis study examines the role of health communication during the COVID-19 pandemic in the Kingdom of Saudi Arabia, with particular emphasis on the influence of mass media especially social media on public perceptions and behaviors. The findings reveal that a high proportion of individuals actively followed media coverage of the crisis, with a significant positive correlation between media exposure and satisfaction with crisis management. A mixed-methods approach was employed, combining quantitative and qualitative tools. A structured questionnaire was administered to 380 participants across five major regions of the country, and in-depth interviews were conducted with key officials from the Ministry of Health and the Ministry of Media. Research instruments were rigorously validated, and data were analyzed using SPSS statistical software. The results highlight the substantial impact of media exposure particularly through social platforms on shaping individual risk perception. However, several challenges were identified, including the dissemination of sensitive or inaccurate information, limited institutional communication resources, and the absence of coordinated crisis response strategies. Accordingly, the study proposes strategic recommendations, including the development of ethical communication protocols, strengthening institutional capacities, and establishing integrated emergency communication units. This research contributes to the growing field of health communication, particularly within the Arab context, and provides a valuable foundation for future academic and institutional advancements in managing communication during public health emergencies.3 0Item Restricted Assessing how Social Media Influences Tourist Decision-Marketing Concerns in Saudi Arabia Related to Vision 2030(Saudi Digital Library, 2025) Albalawi، Ashuq Mohammed; Conyard, EmilyThe context of the research is the novelty of both social media and tourism in the Kingdom of Saudi Arabia, and although “electronic word of mouth (e-WoM)” via social media has an impact in marketing around the world (Bushara et al., 2023), and directly on tourism in the Kingdom of Saudi Arabia (Alsheikh, Aziz and Alsheikh, 2021), the extent of that impact is not known. Studies on the role of social media applications (Al-Hazmi, 2021) and marketing (Bahurmuz and Al-Kubaisy, 2022) on tourism in the Kingdom of Saudi Arabia, have been completed because it is a growth area (Statista, 2023), but other factors of tourism, such as culture, have been more deeply studied in the Kingdom of Saudi Arabia (Madkhali, 2020; Sudigdo and Khalifa, 2020).12 0Item Restricted Keeping Up with Khaleeji Influencers: The Impact of Khaleeji Influencers on Self-Image, Social Comparison, and Beauty Ideals in Young Saudi Women(Saudi Digital Library, 2025) Mohammedali, Melia; Lavertu, LauraThis dissertation explores how Khaleeji beauty influencers on Instagram impact the self- image, emotional wellbeing, and consumer behaviour of young Saudi women. Drawing on Festinger’s (1954) Social Comparison Theory, the study investigates how idealised representations of beauty and lifestyle, particularly when presented by culturally relatable influencers, shape followers’ perceptions and behaviours within Saudi Arabia. Using a qualitative design, the research involved twelve semi-structured interviews and eight visual diaries with Saudi women aged 21–30 (Mage = 24.00, SD = 2.89). Thematic analysis following Saldaña’s (2021) method revealed two overarching themes: (1) Khaleeji vs. Western Influencer Appeal and (2) Impacts of Social Media Platforms. Findings show Khaleeji influencers are both aspirational and relatable, intensifying upward social comparisons and internalisation of beauty ideals. Many participants reported psychological strain, self-monitoring, and changes in purchasing behaviour, including engagement in cosmetic procedures. Cultural and religious values shaped participants’ interpretations of beauty content. Many experienced conflict between modesty norms and glamorous influencer portrayals, especially among hijabi women facing identity dissonance. Religion also acted as a protective filter, guiding selective content engagement and fostering emotional resilience. This study offers a culturally grounded contribution to influencer marketing literature, emphasising how social comparison, digital beauty culture, and local values intersect in non-Western contexts. It provides practical recommendations for influencers, brands, and social media platforms to foster ethical, inclusive, and culturally sensitive content.5 0Item Restricted Influence of social media on perceptions of oral health, dental appearance, self-esteem, and dental treatment decisions: a scoping review(Saudi Digital Library, 2025) Alasmari, Osama Ali; Heilmann, AnjaBackground: Social media has emerged as a pervasive force, with some emerging research examining its influence on public perceptions and treatment-seeking behaviours in relation to dentistry. While offering opportunities for health promotion, platforms like Instagram and TikTok also amplify unrealistic aesthetic standards, contributing to dissatisfaction with dental appearance and the spread of misinformation. A small body of literature on this topic now exists, which so far has not been reviewed, creating the need for a comprehensive synthesis of this fragmented evidence. Aim: To undertake a scoping review of the evidence on the influence of social media on perceptions of oral health, dental appearance, self-esteem, and subsequent dental treatment decisions. Methods: A scoping review was conducted, using a systematic search of the PubMed database. Following a rigorous screening process, a final synthesis of 11 studies published between 2022 and 2025, covering diverse geographical regions including the Middle East, Europe, and Asia, was undertaken. Results: The findings reveal three key themes. First, across the available studies, exposure to idealised images on social media is strongly associated with increased dissatisfaction with one's own dental appearance and a decline in facial satisfaction. Second, this cultivated dissatisfaction acts as a significant driver for seeking aesthetic dental treatments, with a sizable proportion of participants citing social media as a direct influence on their decision-making. Finally, a critical "trust paradox" was identified: while general trust in social media for health information correlates with a higher prevalence of oral health misconceptions, following accounts of verified dental professionals is associated with a significant reduction in such beliefs. Conclusion: The available evidence suggests that social media is a dual-edged force within modern dentistry that influences patients' perceptions and treatment-seeking behaviours. The findings highlight an urgent need for public health initiatives focused on enhancing digital health literacy and for professional guidelines to be developed by dental regulatory bodies. These measures are essential to combat misinformation and empower patients to make informed, health-conscious decisions.30 0Item 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.7 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.17 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.42 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.19 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.32 0
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