Using Twitter to Learn about the Autism Community 5 tionable [9] and poorly supported by evidence (e.g. gluten-free and casein-free diets, and cognitive behavioural therapy [10]), and sometimes outright in conflict with science [11]. For example a recent review of early intensive behavioural and developmental interventions for young children with ASD found 1 existing study as being of good quality, 10 as fair quality, and 23 as poor quality [9]. From the public policy point of view, understanding the practices and beliefs of parents and carers of ASD-affected individuals is crucial, yet often lacking [12]. Thus the key idea motivating the present work is that the rapid rise in the adoption of social media as a platform for the expression and exchange of ideas, which facilitates the emergence of special interest communities, can be used to study and monitor the beliefs and practices of the population affected by ASD. Considering the challenge of reaching and engaging with this specific target population, our findings pave the way for further work of potential significant benefit to public health. These benefits include the enrichment of the corpus of knowledge of the condition itself by the medical community, and the increased understanding of the practices and concerns of those affected by ASD. While this is the first work exploring the possibility of using Twitter data for the extraction of ASD-related information, the broad idea of data-mining Twitter is not new and has been employed successfully in a variety of applications. At the same time it is important to stress at the very outset that most of the previous work on this topic has not been automatic, that is, analysis was performed ‘manually’ by humans. This is a laborious process which severely limits how much data can be processed. Additionally, the use of human intelligence rather than computer-based methods means that in the case of many reported results, it is not clear that the same results could be obtained automatically due to a possible semantic gap. A popular research direction focuses on various forms of prediction based on tweet sentiments [13] inferred from the tweet’s emoticons [14] or using linguistics-based classi- fiers [15]. For example, Asur and Huberman [16] showed that tweet posting rate can be used to forecast film box office revenues, while Baucom et al. [17] used sentiment to analyse the 3 Previous work