13 Using Twitter to Learn about the Autism Community Fig. 3: (a) A log-log plot showing the dependence of tweet term (word) frequency on its fre- quency rank within the corpus of all terms in our data set. Approximately linear dependency is exhibited by data extracted from both the ASD and the control data sets, in agreement with the Zipfian power law distribution. (b) Linear dependency is quantified using the Pearson’s r statistic. To account for the expected artefacts at the ends of the distribution, the statistic is plotted as a function of the width of the window centred at the logarithmically median rank and spanning 20–100% of the logarithmic range of possible ranks. Pearson’s r attains values of over 0.995 for the central portion of the distribution, and drops slightly (to approximately 0.992) for the entire range when the end artefacts are included. 10 0 10 1 10 2 10 3 10 4 10 5 10 6 Term rank 10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 Term frequency ASD Control (a) 20 40 60 80 100 Window width (%) 0.990 0.992 0.994 0.996 0.998 1.000 Pearson's r ASD Control (b)