20 Adham Beykikhoshk, Ognjen Arandjelovi´c, Dinh Phung, Svetha Venkatesh, and Terry Caelli group would engage more in the discussion of so-called celebrities. A more thorough, man- ual examination of relevant tweets reveals the reason – while the ASD group does engage in less chatter about celebrities, this is offset by the substantial attention that autistic peo- ple, specific autism activists, or politicians commenting or acting on autism-related issues receive. In Section 4.2.1 we provided evidence that tweets, including those made by the commu- nity interested in the ASD, obey some of the same general regularities when it comes to the pattern of word usage. Then, starting in Section 4.3 and corroborating this further in Sections 4.4.1, 4.4.2, and 4.4.3, we showed that in terms of their semantics and content, messages of the ASD community appear to exhibit a range of characteristics which differ- entiate them from those in our control data set. This motivated us to explore if it is possible to classify tweets automatically as belonging to the ASD data set or not. It is important to emphasise right at the beginning that in this experiment we elimi- nated from all tweets the five search keywords which we used to divide our entire tweet data corpus into ASD and control sets i.e. which we used to define the quasi-ground truth labelling. Recall from Section 4.1 that these keywords are “autism”, “adhd”, “asperger”, and “aspie”. Had they not been removed, classification performance would have been artificially increased as their presence in a tweet would have been learnt as being capable of perfectly predicting classification output. Furthermore, it is reasonable to expect that the ‘true’ ASD corpus of tweets should include some tweets which do not contain any of the aforemen- tioned five keywords. In that sense, we hypothesised that it would be possible to use simple tweet filtering based on a small number of obvious keywords to build a bootstrap training data set which could be in turn used to learn additional aspects characterizing ASD-related tweets, thereby facilitatating a more robust retrieval of messages of interest. Discriminative power of individual terms To examine the discriminative potential of tweet content following the removal of our keywords, we first looked at the discriminative power 4.5 Tweet classification