KAUST encouraged attendees of the 2015 Winter Enrichment Program (WEP) to share their experiences on social media using the hashtag #wep2015. The university provided tips for participants to effectively use platforms like Facebook, Twitter, and Instagram during the event. KAUST emphasized responsible sharing and respect for the university's multicultural community when posting. Why it matters: This initiative aimed to amplify the reach of WEP's activities and engage a broader audience in KAUST's community and knowledge-sharing efforts.
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.
A KAUST research team is using cellphone mobility data, Google searches, and social media to model and predict COVID-19 spread. The models aim to forecast cases in the coming weeks and inform resource allocation, including hospital beds and medical staff. The team is using aggregated and anonymized data from cellphone companies to respect people's privacy. Why it matters: Integrating real-time digital data with epidemiological modeling can improve the speed and effectiveness of public health responses in the region and globally.
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.
Researchers studied user lifetime prediction in the location-based social network Jodel within Saudi Arabia, leveraging its disjoint communities. Machine learning models, particularly Random Forest, were trained to predict user lifetime as a regression and classification problem. A single countrywide model generalizes well and performs similarly to community-specific models.