Using Arabic Social Media Feeds for Incident and Emergency Management in Smart Cities

Research on Smart Cities tackles the challenges related to the rapid urban population growth combined with resources' scarcity. A key function of any Smart City initiative is to be able to continuously monitor and track a city's environment and resources so as to convert the data into intelligence for streamlining the city's operations. Social media has become one of the most popular means to allow users to communicate and share information, opinions, and sentiments about events and incidents occurring in a city. With the rapid growth and proliferation of social media platforms, there is a vast amount of user-generated content that can be used as source of information about cities. In this work, we propose the use of text mining and classification techniques to extract the intelligence needed from Arabic social media feeds, for effective incident and emergency management in smart cities. In our system, the information collected from social media feeds is processed to generate incident intelligence reports, including information such as: the event type; the event stage, the impact level, the environmental conditions on the incident scene; and the number of people impacted. Such real-time generated reports can be used by rescue teams for fast assessment and effective response to incidents and emergencies occurring in the city. The proposed algorithm was implemented and tested using datasets collected from Arabic Twitter feeds, and the obtained results are very promising.