Using Twitter to Learn about the Autism Community 19 the control tweets. This observation led us to investigate this matter in further detail. In particular, we compared the usage of different part-of-speech (POS) types in the two data sets. We used the free TweetNLP software package [42], trained on Twitter data, to tag auto- matically all terms in our entire data set according to their POS type. A selected set of the most interesting results is shown in the plots in Fig. 7. The first thing to observe from the entire corpus of results is that there is a consistent difference between the ASD and control tweets. Notwithstanding the large standard deviation values and the significant overlap of distributions corresponding to ASD and control tweets, that the difference between the two corpora is real rather than a result of stochasticity, is readily witnessed by the fact that in each of the plots the same relative behaviour is exhibited regardless of the tweet length. We confirmed this statistical significance rigorously too. In order to collate all data shown in a single plot, rather than considering the actual POS type count per tweet we considered POS type count normalized by tweet length. This allowed us to perform a single Student’s t -test per POS type (i.e. plot in Fig. 7). In all cases we obtained p < 0 . 05 . Next we turned our attention to the interpretation of the specific results in Fig. 7. Note by observing the plots in Fig. 7(a) and Fig. 7(b) that our hypothesis from Section 4.4.1 regarding the type of information communicated within the ASD and control sub-groups, is confirmed. Recall that by examining qualitatively word clouds of the dominant terms in the two corpora we hypothesised that the tweets in the ASD sub-group are more focused on issues (syndromes, disorders, interventions, support, help, and so on) and individuals (mostly children), described by nouns, while the control tweets more often relate to what it is that their authors are doing (liking, wanting, going, thinking, etc). Indeed for the average tweet length (as counted by the number of words in a message) of 15-20 terms, the number of nouns in the ASD data set is about twice the number of nouns in the control data set. The difference in the number of verbs, while not as substantial, is also significant – for the average length message, the control data set contains approximately 20% more verbs. In- terestingly, the number of proper nouns does not appear to differ significantly between the two groups. This is perhaps somewhat surprising, as it could be expected that the control