API allowed us to obtain ‘control’ data, unrelated to ASD, since this set could not be well characterized a priori using a compact set of keywords. 

We collected only tweets posted in English. To facilitate a comparative analysis, we collected two non-overlapping data sets. The first of these, which we will refer to as the ASD subset, comprises tweets which concern ASD. Specifically, we defined the ASD subset as comprising tweets which contain any of the four keywords “autism”, “adhd”, “asperger”, and “aspie” (or any of their derivatives obtained by suffixation), and the control subset as comprising all other tweets. In total this resulted in a corpus of 5,650,989 ASD-related tweets collected in the period starting on 26 August 2013 and ending on 1 Oct 2014 (i.e. more than 13 consecutive months). Of these, 3,493,742 were original tweets which were produced by 1,771,274 unique users, with the remaining 28% of the messages being re- tweets. Approximately 25% of the collected tweets (2,260,284) contain so-called hashtags (see Section 4.4.2 for discussion and analysis). Although this information was not used in the present work we also report that in our corpus 70,925 tweets are geo-tagged (it should be noted that geo-tags, being controlled entirely by the user, are not necessarily correct location identifiers), 2,599,395 contain URLs, 464,190 were sent in reply, and 3,330,096 mention another user. 

It is important to observe that it is not our claim that tweets in the ASD subset were necessarily posted by individuals suffering from ASD. While some of the messages in this subset do fall into this category, the subset will also include posts by individuals affected by ASD in a looser sense, e.g. parents or carers of those who suffer from ASD, or indeed medical professionals interested in the condition. 

## 4.1.1 Pre-processing 

Much of the work in the present paper concerns the analysis of topics discussed by means of Twitter. In this context it is beneficial to have different inflections of the same word normalized and represented by a single term. In linguistics this process is referred to as lemmatization and we apply it automatically using the freely available Natural Language Toolkit (NLTK) [35]. In addition, we remove the so-called ‘stop words’ which do not carry 

