Adham Beykikhoshk, Ognjen Arandjelovi´c, Dinh Phung, Svetha Venkatesh, and Terry Caelli 22 of individual words. This is effectively visualized in Fig. 8. What the plot shows is the most frequent 1300 words in our entire data set, which covers respectively 75% and 83% of the words in ASD and control subsets, depicted as circular blobs and colour-coded by their discriminative power (specifically, the saturation of a blob’s colour is proportional to the absolute value of the logarithm of the ASD-control likelihood ratio of the corresponding word). As expected based on our previous results presented in Sections 4.4.1 and 4.4.2, a number of words pertaining to issues of most concern to the ASD community are highly dis- criminative. This observation motivated our next set of experiments which examined tweet classification in detail. Fig. 8: A compact illustration of the discriminative power of different individual words. Each circular blob represents a word, the saturation of its colour being proportional to the absolute value of the logarithm of the ASD-control likelihood ratio of the word. 10 -4 10 -3 10 -2 10 -1 Term frequency (ASD group) 10 -4 10 -3 10 -2 10 -1 Term frequency (Control group) be atuser rtuser you me love go do child adult help kid me url son parent support speak study add program raise brain focus education teen risk training season hair hot ill birthday wanna bitch flower