We also experimented with the application of latent semantic analysis (LSA) [50], test- ing out the possibility that better discrimination may be achieved by working in the so-called concept rather than term space. Although LSA has been shown to be highly successful in a variety of information retrieval tasks, our experiments suggest that the same benefit is not observed when it is applied on tweets. The likely reason for this can be found in the brevity of tweets, which favours the use of succinct expressions whereby individual terms (unigrams) themselves effectively become LSA concepts. This also explains why the simple na¨ıve Bayes classifier performed on par with the generally superior logistic regression-based one, as can be seen in Table 1. Adham Beykikhoshk, Ognjen Arandjelovi´c, Dinh Phung, Svetha Venkatesh, and Terry Caelli 28 We next evaluated the LASSO-based classifier. In order to examine fully the behaviour of this approach we did not perform the usual procedure for selecting the value of the free parameter λ using cross-validation; rather we measured classification performance across a range of λ values. Specifically we considered values from λ = 10 e − 5 up to λ = 10 e − $^{2}$; greater values than 10 e − 2 produced numerical problems caused by an excessively small set of selected predictor terms. Our results are summarized in Fig. 9. The plot shows that the classifier exhibits consistent performance across the range of λ between 10 e − 5 and 10 e − $^{4}$, with the accuracy of the classification of ASD tweets deteriorating thereafter (with a small increase in the accuracy of control tweet classification). This deterioration is readily ex- plained by examining the number of terms selected by LASSO i.e. by the number of terms with non-zero corresponding regression coefficients. For λ = 10 e − 5 this number is 801, dropping down for λ = 10 e − 4 to 580, and reducing drastically to 151 for λ = 10 e − 3 and to 12 for λ = 10 e − 2 as a consequence of the harsh penalty on model complexity effected by the large values of λ . Lastly, useful insight can be gained by considering the terms which correspond to the largest magnitude regression coefficients. These are, in order, “help”, “kid”, “aw” (the au- tomatically constructed stem of words such as “awareness” and “aware”), and “child”. The corresponding regression coefficients are all positive i.e. the presence of these terms pre- dicts ASD-related tweets. Consequently, control tweets are predominantly predicted by the absence rather than presence of specific terms; the highest magnitude negative coefficient,