2 Adham Beykikhoshk, Ognjen Arandjelovi´c, Dinh Phung, Svetha Venkatesh, and Terry Caelli Keywords Social media · Big Data · Asperger’s · Mental health · Health care · Public health · ASD In this paper we are interested in leveraging the remarkable increase in the use of social me- dia, and Twitter in particular, to obtain information about specific demographic communities which are difficult to reach by conventional means. To illustrate our ideas and demonstrate their effectiveness, herein we focus on the population affected by the autism spectrum disor- der (ASD) – a neurodevelopmental disorder which has been attracting increasing attention as much for its complex and varied aetiology, as for the associated and rapidly growing so- cioeconomic burden. The population of interest in this work includes both individuals who have been diagnosed with ASD themselves, as well as those who are indirectly but signifi- cantly impacted such as the family members and carers of ASD sufferers. To the best of our knowledge ours is the first work of this nature. The present paper builds on the preliminary results reported in [1] and describes a number of novel findings and experiments whose re- sults further support the key underlying idea and motivate additional work in this direction. Most notable novelties include: 1 Introduction – additional statistics of the collected tweet corpus, – quantitative analysis of Zipf’s law for tweets and ASD-related tweets in particular, – examination of statistical significance of differential part-of-speech characteristics, – experiments using least absolute shrinkage and selection operator (LASSO), – additional evidence of content saliency using an Alzheimer’s disease tweet corpus, and – new analysis of the procedure for the selection of the bootstrap keyword set.