Using Twitter to Learn about the Autism Community 31 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). 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 4.6 Bootstrap keyword set analysis Table 2: A summary of the classification results (ASD-related vs. Alzheimer disease-related tweets). Each cell shows the associated confusion matrix. The first row/column of a con- fusion matrix corresponds to the Alzheimer’s disease class and second row/column to the ASD class. Confusion matrix Representation Term count Na¨ıve Bayes 0 . 98 0 . 02 0 . 05 0 . 95 Logistic regression 0 . 98 0 . 02 0 . 00 1 . 00