11 Using Twitter to Learn about the Autism Community analysis [37], to information retrieval [38] and quantitative linguistics [38]. In quantitative linguistics, an event corresponds to the usage of a particular word or, more precisely in the context of the present paper, a particular term resulting from the pre-processing of a word as described in Section 4.1.1. Evidence from previous work suggests that conventional texts, such as newspapers or books, do result in Zipfian distributions of word frequencies [38]. However, it is not clear from this that the same applies to tweets. Firstly, tweets are restricted in length to 140 char- acters which by itself may alter linguistic characteristics of posted messages. In addition, the nature of Twitter as a communication medium invariably introduces a self-selecting aspect: neither can the corpus of Twitter users be considered to be a random sample from the pop- ulation, nor can the memetic content of tweets be expected to match that of texts examined by previous work, such as newspapers and books. In our data set, the ASD corpus of tweets contains 47,048,097 terms (pre-processed words) of which 402,946 are unique; the control subgroup used 32,846,321 terms of which 528,755 are unique. The key findings are summarized in Fig. 3(a) which shows the plot of tweet term frequency as a function of its overall term frequency rank. It is readily apparent that the characteristics of both the ASD and the control group are nearly identical. What is more, both can be seen to exhibit approximately linear behaviour on this plot; a mild deviation from this behaviour can be observed for the most frequent terms. There are two main reasons for this. Firstly, it is generally the case that the simple functional form of Zipf’s law fails to be observed for the top-ranking events. Secondly, our use of the logarithmic scale for the abscissa means that the left-hand tail of the graph is sparsely populated by data points which increases the corresponding error margins in this plot. To quantify the linearity of dependence plotted in Fig. 3(a) we used Pearson’s r statistic. Specifically, we computed the value of the statistic first for the window which spans 20% of the ranks on the logarithmic scale and is centred at the logarithmic median of the ranks (i.e. the centre of the plot in Fig. 3(a)), and then proceeded to increase the width recomputing the value for the encompassed range until all ranks were included. The variation of Pearson’s r as a function of the window width is plotted in Fig. 3(b) confirming our previous observa-