34 Adham Beykikhoshk, Ognjen Arandjelovi´c, Dinh Phung, Svetha Venkatesh, and Terry Caelli Fig. 10: Classification results using only a subset of the entire training corpus used in the pre- vious section. In each experiment training was performed using tweets matched by the three remaining keywords after one of the original keywords was left out. For each experiment we show three classification accuracies: on (i) test corpus control tweets (green bars), (ii) test corpus ASD-related tweets (blue bars), and (iii) tweets matched by the left out keyword but no others (red). See main text for a discussion of the findings. of messages in the two subsets and reported a series of novel results. The most important finding, corroborated by several different experiments, concerns the nature of topics arising in the ASD subset of tweets. We demonstrated that tweets in this subset are very rich in information of potentially high value to public health officials and policy makers, thus mo- tivating further work towards our goal of developing a tool which would be able to monitor automatically the response of the ASD community to various initiatives, legislature, medical advances etc. For example, driven by the finding that a high proportion of the tweets in our corpus contain URL links, one of the directions we wish to pursue in future is that of ex- ploring the value of auxiliary information which can be associated with original messages. We also intend to explore automatic ways of labelling topics semantically using meaningful sentence fragments by back-analysing probabilistically the collected text data for persistent ngrams across documents with shared topics [51]. autism adhd asperger aspie Left out terms 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Accuracy of the classifier Control ASD Left Out