Adham Beykikhoshk, Ognjen Arandjelovi´c, Dinh Phung, Svetha Venkatesh, and Terry Caelli 16 sponding to the ASD group which are related to children such as “children”, “kids”, “child”, and “son”, for example. There are a number of reasons why this is unsurprising. Firstly, the diagnosis of ASD is usually made in early childhood so it is reasonable to expect that the parents confronting this new challenge would have increased initiative at seeking help from the community of parents in a similar situation. In addition, while undoubtedly always vul- nerable, the vulnerability of individuals on the autism spectrum is the greatest while they are young which is when they need the most support from their guardians and therapists e.g. at acquiring the skills needed to progress thorough the educational system and integrate in the society. It is also interesting to observe the high frequency of the words “son” and “boy” in the ASD data (top-right section of the word cloud), and the absence of the equivalent female sex words “daughter” and “girl”. This example illustrates well how the content of tweets can be used to extract some rather subtle information. In particular in this case our findings are consistent with the understanding of the medical community and a body of evidence which shows that boys are nearly five times more likely to suffer from autism than girls [39]. In contrast, the most frequently used words in the control group do not seem to follow any particular pattern or focus of interest, and instead pertain to more general everyday inter- ests and activities. Lastly, it is interesting to observe that the set of words in Fig. 5(a) appears to contain more nouns than Fig. 5(b), and fewer verbs. This suggests a different nature of information exchange in the ASD group which appears more focused on issues (syndromes, disorders, interventions, support, help, and so on) and individuals (mostly children), both being described by nouns, while the users who posted tweets of the control group seem to be more interested in what it is that they are or will be doing (like, want, go, think, and so on). We will explore this quantitatively in more detail in Section 4.4.3. An alternative proxy for tweet content can be found in user-designated tags, the so-called ‘hashtags’. These can be recognized by the leading special character ‘#’ (the hash sign) and are by convention understood to be meta-data labels in some manner connected with 4.4.2 Hashtag analysis