Abstract
Road transportation is the backbone of modern economies despite costing annually millions of human deaths and injuries and trillions of dollars. Twitter is a powerful information source for transportation but major challenges in big data management and Twitter analytics need addressing. We propose Iktishaf, developed over Apache Spark, a big data tool for traffic-related event detection from Twitter data in Saudi Arabia. It uses three machine learning (ML) algorithms to build multiple classifiers to detect eight event types. The classifiers are validated using widely used criteria and against external sources. Iktishaf Stemmer improves text preprocessing, event detection and feature space. Using 2.5 million tweets, we detect events without prior knowledge including the KSA national day, a fire in Riyadh, rains in Makkah and Taif, and the inauguration of Al-Haramain train. We are not aware of any work, apart from ours, that uses big data technologies for event detection of road traffic events from tweets in Arabic. Iktishaf provides hybrid human-ML methods and is a prime example of bringing together AI theory, big data processing, and human cognition applied to a practical problem.