Using Twitter to Learn about the Autism Community 29 10 -5 10 -4 10 -3 10 -2 λ 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Accuracy Control ASD Fig. 9: LASSO-based classification performance as a function of the loss-sparseness tradeoff parameter λ . The number of non-zero regression coefficients for λ equal to 10 e − $^{5}$, 10 e − $^{4}$, 10 e − $^{3}$, and 10 e − 2 is respectively 801, 580, 151, and 12. High values of λ which severely penalize model complexity can be seen to result in deteriorating classification performance for ASD-related tweets. that is coefficient which corresponds to the term which predicts a control tweet, is “follow”. The observation that control tweets are predicted by the absence of ASD-related terms ex- plains why a reduction in model complexity (i.e. a reduction in the number of ASD-terms considered by the classifier) does not produce a deterioration in classification performance on control tweets (rather, a small improvement can be noticed) while a major negative effect is readily observed for the ASD corpus. Condition-oriented classification: results and discussion To motivate our next set of experi- ments, consider a classifier which distinguishes between medical condition-related and other tweets. Let the classifier correctly classify a tweet of the former type (dependent variable value +1 ) with the probability p$_{m}$ and of the latter type with the probability p$_{n}$ (dependent variable value − 1 ). Imagine applying this classifier on our data set but interpreting its output