14 Adham Beykikhoshk, Ognjen Arandjelovi´c, Dinh Phung, Svetha Venkatesh, and Terry Caelli 0 10 20 30 40 50 Tweet word count 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 Normalized frequency ASD Control Fig. 4: Normalized histograms of tweet word counts for the two subsets. As expected, log- normal distributions are observed but, importantly in the context of the present work, with different parameters (most obviously the means, p < 0 . 01 ). Our next aim was to explore if the collected data offers evidence that tweets in the ASD and control data sets differ significantly by their content (i.e. topic of conversation) and if so, if automatic methods could be employed in the analysis of this content. In the first experiment we approached this task by comparing the most frequently used words in the two data sets. These words can be seen as a simple cumulative proxy for the actual content of individual messages. The key results are illustrated in Fig. 5(a) and Fig. 5(b) which show the 100 most frequent words in respectively the ASD and control data sets, displayed as so-called ‘word-clouds’ whereby the frequency of a particular word is encoded 4.4 Content analysis 4.4.1 Word frequency