A growing body of literature has been devoted to harnessing the crowdsourcing power of social media by extracting knowledge from the huge amounts of information available online. This paper discusses how social media data can be used indirectly and with minimal cost to extract travel attributes such as trip purpose and activity location. As a result, the capacity of Twitter data in complementing other sources of transport related data such as household travel surveys or traffic count data is examined. Further, a detailed discussion is provided on how short term travellers, such as tourists, can be identified using Twitter data and how their travel pattern can be analysed. Having appropriate information about tourists/visitors - such as the places they visit, their origin and the pattern of their movements at their destination - is of great importance to urban planners. The available profile information of users and self-reported geo-location data on Twitter are used to identify tourists visiting Sydney as well as also those Sydney residents who made a trip outside Sydney. The presented data and analysis enable us to understand and track tourists' movements in cities for better urban planning. The results of this paper open up avenues for travel demand modellers to explore the possibility of using big data (in this case Twitter data) to model short distance (day-to-day or activity based) and long distance (vacation) trips.