Social Media Analytics Platforms

Abstract

Digital societies could be characterised by their increasing desire to express themselves and interact with others. This is being realised through digital platforms such as social media that have increasingly become convenient and inexpensive sensors compared to physical sensors in many sectors of smart societies. One such major sector is road transportation, which is the backbone of modern economies and costs globally 1.25 million deaths and 50 million human injuries annually. The cutting-edge on big data-enabled social media analytics for transportation-related studies is limited. This chapter brings a range of technologies together to detect road traffic-related events using big data and distributed machine learning. The most specific contribution of this research is an automatic labelling method for machine learning-based traffic-related event detection from Twitter data in the Arabic language. The introduced method has been implemented in a software tool called Iktishaf+ (an Arabic word meaning discovery) that is able to detect traffic events automatically from tweets in the Arabic language using distributed machine learning over Apache Spark. The tool is built using nine components and a range of technologies including Apache Spark, Parquet, and MongoDB. Iktishaf+ uses a light stemmer for the Arabic language developed by us. The tool also uses a location extractor developed by the team that allows us to extract and visualise spatio-temporal information about the detected events. The specific data used in this work comprises 33.5 million tweets collected from Saudi Arabia using the Twitter API. Using Support Vector Machines, Naive Bayes, and Logistic Regression based classifier, we are able to detect and validate several real events in Saudi Arabia without prior knowledge including a fire in Jeddah, rains in Makkah, and an accident in Riyadh. The findings underlined the effectiveness of Twitter media in detecting important events with no earlier knowledge about them.