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Scaling-Laws of Human Broadcast Communication Enable Distinction between Human, Corporate and Robot Twitter Users

Figure 1

Plots illustrating the methods used for the computation and evaluation of the predictive algorithms.

(a) The CDF computed for the personal accounts class using accounts is shown in red, while the step functions computed for 5 tweets of the left-out account are shown in blue. The CDF corresponds to the probability that a tweet will be posted seconds after the previous tweet (predicted probability), while the step functions correspond to the observed probability for the occurrence of tweets (observed or actual probability). A perfect prediction for a specific tweet would mean that the CDF coincides exactly with the step function for that tweet. (b) In this histogram, the axis on the left of the plane corresponds to the value of the CDF obtained for the inter-tweet delay (predicted value), while the axis on the right corresponds to the value of the step function obtained for the same delay (actual value, which is either 0 or 1). A perfect predictive model would have all data points grouped in bins and , indicating that the CDF models the step functions exactly and thus all predicted and actual values coincide. The fact that these two bins have much higher probabilities than all others in the histogram illustrates the model's accuracy.

Figure 1