12 Adham Beykikhoshk, Ognjen Arandjelovi´c, Dinh Phung, Svetha Venkatesh, and Terry Caelli tions. The linearity of the central portion of the distribution is nearly perfect as witnessed by the r value of 0.999. As more data is added a slight non-linearity is observed, the statistic dropping down to approximately 0.992 for the entire range. In addition to their novelty, these findings are interesting in terms of directing our re- search towards our ultimate goal of automatically retrieving and analysing the content of ASD-related tweets. In particular, the discovery that tweets, including those in the sub-group of our primary interest, obey Zipf’s law suggests that it is sensible to adopt and explore the use of a broad range of well-known and well-understood text representations and methods of analysis. Indeed we do this next. Having established that Twitter messages conform to some of the same general linguistic rules as conventional texts do, our next goal was to explore any differential characteristics exhibited by our ASD and control groups. Recall from Section 4.1 that our data set is bal- anced in the number of tweets, that is, the number of tweets in the ASD corpus is the same as in the control corpus. Yet, as pointed out in the previous section, the term counts in the two data sets are different, respectively 47,048,097 and 32,846,321 – a significant difference of approximately 43%. It is a direct implication of this observation that the average term count per tweet is greater in the ASD group, which is the first indication of there being a differ- ence between tweets in this group and the remainder of our data corpus. The tendency of this group to post longer messages is further corroborated by comparing the corresponding histograms of tweet word counts, which are shown in Fig. 4. While the tweet word count in both sub-groups can be seen to be log-normally distributed, the two distributions have substantially different means ( p < 0 . 01 ). 4.3 Message length