Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city services including energy, transportation, health, and much more. They generate massive volumes of structured and unstructured data on a daily basis. Also, social networks, such as Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart cities. Social network users are acting as social sensors. These datasets so large and complex are difficult to manage with conventional data management tools and methods. To become valuable, this massive amount of data, known as 'big data,' needs to be processed and comprehended to hold the promise of supporting a broad range of urban and smart cities functions, including among others transportation, water, and energy consumption, pollution surveillance, and smart city governance. In this work, we investigate how social media analytics help to analyze smart city data collected from various social media sources, such as Twitter and Facebook, to detect various events taking place in a smart city and identify the importance of events and concerns of citizens regarding some events. A case scenario analyses the opinions of users concerning the traffic in three largest cities in the UAE