Using Twitter to Learn about the Autism Community 7 spread of epidemic influenza can be predicted and tracked from the location and demo- graphic information of users of relevant tweets. A similar approach was also described by Li and Cardie [32]. Yet further evidence of the power of Twitter data was presented by Chew and Eysenbach [33] who demonstrated that the spatio-temporal distribution of relevant tweet frequencies during the 2009 H1N1 outbreak closely matches the disease spreading pattern. Although the use of Twitter for data-mining information related to ASD has not been explored yet, there has been some preliminary work on the use of other social media and ASD. For example, Newton et al. [34] used Linguistic Inquiry and Word Count (LIWC) dictionaries to compare writing patterns of individuals with ASD and those of neuro-typical bloggers. Having outlined the motivation and our ultimate vision for this work, and placed it in context of previous research on data-mining social media, we now turn our attention to the main contribution of the present paper. We start by describing the data set we used for our analysis. Twitter’s Terms of Service explicitly prohibit the sharing or redistribution of tweets, includ- ing for research purposes. Consequently, there was no public data set that we could use as a standard benchmark in this study. Instead, we collected a large data set ourselves. Twitter API offers different means of retrieving tweets. In particular, we used its ‘search’ and ’streaming’ functions. The former allows the retrieval of historical tweets based on the presence of specific keywords and meta-data constraints (e.g. on the language or user lo- cation). After being posted, a tweet can be obtained in this manner for up to a week. The streaming API allows a quasi-realtime retrieval of tweets as they are posted, retrieving a sample of approximately 1% of all tweets. The search API was most valuable for us for col- lecting ASD-related content, as we will describe in detail shortly. Conversely, the streaming 4 Methods, results, and discussion 4.1 Data acquisition