However, the long tails characterizing distributions such as the Gutenberg–Richter law or the words-occurrence Zipf's law, and those highlighted by Anderson and Shirky are of very different, if not opposite, nature: Anderson and Shirky refer to frequency-rank relations, whereas the Gutenberg–Richter law and the Zipf's law are probability distributions. Therefore, in these latter cases "tails" correspond to large-intensity events such as large earthquakes and most popular words, who dominate the distributions. By contrast, the long tails in the frequency-rank plots highlighted by Anderson and Shirky would rather correspond to short tails in the associated probability distributions, and therefore illustrate an opposite phenomenon compared to the Gutenberg–Richter and the Zipf's laws.