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their number with the number of ASD-related tweets in our data set. Considering the little difference in performance obtained by the use of different representations in our previous set of experiments, we did not examine them all here too; instead we adopted the term count representation as the most commonly used one in the literature. 

Our results are summarised in Table 2. It can be readily observed, especially in the con- text of the evidence presented previously, that this experiment too strongly supports our thesis that the content of ASD tweets, even with the most salient words removed (the key- words used to collect the corpus as described in Section 4.1) is highly informative. Both Alzheimer’s disease-related and ASD-related tweets were classified with nearly perfect ac- curacy. As in our previous experiments there was little difference between the performance of the na¨ıve Bayes classifier and that based on logistic regression, the latter achieving some- what better results on ASD-related tweets (100% vs. 98% accuracy). 

4.6 Bootstrap keyword set analysis 

Recall from Section 4.1 that we collected the ASD tweet corpus by including in it any tweet which contained any of the four keywords “autism”, “adhd”, “asperger”, and “aspie” (or 

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