This paper explores how AI and social media analytics can identify and track trends in Saudi Arabia across sectors such as construction, food and beverage, tourism, technology, and entertainment. The study analyzed millions of social media posts each month, classifying discussions and calculating scores to track trends. The AI-driven methodology was able to predict the emergence and growth of trends by utilizing social media data.
This paper explores the use of AI and social media analytics to detect sustainability trends in Saudi Arabia's evolving market, in line with Vision 2030. The study processes millions of social media posts, news articles, and blogs to understand sustainability trends across various sectors. The AI-driven methodology offers sector-specific and cross-sector insights, providing decision-makers with a snapshot of market shifts, and can be adapted to other regions.
Lucidya, a startup founded by Saudi entrepreneurs including KAUST alumnus Zuhair Khayyat, utilizes AI and Big Data to analyze social media content from platforms like Twitter and Facebook, as well as articles from 200 million websites in over 120 languages. The technology predicts user emotions, detects interests, and provides content analyses to customers for better decision-making. Lucidya commercially transformed the scientific research 'Tagreed' to start their company. Why it matters: This demonstrates the growing potential of Saudi startups in leveraging AI for data analysis and social media monitoring, and it showcases the role of KAUST in fostering technological innovation and entrepreneurship within the Kingdom.
This paper introduces a new task: detecting propaganda techniques in code-switched text. The authors created and released a corpus of 1,030 English-Roman Urdu code-switched texts annotated with 20 propaganda techniques. Experiments show the importance of directly modeling multilinguality and using the right fine-tuning strategy for this task.