## 4 Discussion  
The results of the Hurst parameter, as seen in Figure 2, illustrate that more than 75% of the Hurst parameter values for scientific texts are mainly agglomerated in the interval (0.5, 0.7], suggesting that scientific texts represent persistent phenomena and possess self-similarity. This implies the existence of an intrinsic order that resembles natural phenomena. A _persistent text_ has memory i.e., a pattern occurs at time \(x\), then at a time \(n(x+\delta)\), as a result of the original event at time \(x\). It means that events in the future are highly correlated to events in the past. This can be explained by the repeatability of sentences as part of the rhetorical structure of scientific texts. Thus, in scientific texts main ideas are presented, and then explained with more and more details. However, every time such main ideas are put forward, they are actually expressed with the same set of concepts, forming a pattern that repeats along the time axis.  
Interestingly, business articles also classify as persistent phenomena, but this suggestion is not as strong as it is for scientific articles. The median of the Hurst parameter for this category is 0.54, while the median of the Hurst parameter for scientific texts is 0.57. It can be stated that 75% of the business texts analyzed represent persistent processes, but the upper value in this category is smaller than the upper value in scientific texts.  
The median value of the Hurst parameter for randomly-generated texts is 0.48. Interestingly, fifty percent of the random texts are compressed in a very narrow interval. This fact signifies that random texts represent random walk processes. In this analysis a document categorized as random walk is one filled with words having no relation among them. As a matter of fact, randomly-generated texts are a clear example of a Brownian motion.  
Figure 3 indicated that, for texts whose Hurst parameter lies in the interval [0.45, 0.54], the probability to be a random text is the highest. The second most probable type of text would be entertainment.  
The median for diffusion of science documents lies in 0.49. It means that half the sample is persistent and the other half is anti-persistent. The median values of the Hurst parameter for entertainment texts is 0.53, with a non-symmetrical distribution. Thus, diffusion of science and entertainment news reflect both, anti-persistence and persistence processes. Anti-persistent texts possess no memory, that is to say, the written discourse is elaborated with sentences that do not form linguistic patterns along the time axis. Anti-persistence processes exhibiting higher noise and more volatility than persistence processes.  
In summary:  
* Scientific texts represent persistent processes, where the trend of previous steps are likely to be kept i.e., the text is _self-similar_, and it possesses long-term memory.
* Business articles are self-similar. However, the values of the Hurst parameter are lower than those of scientific texts.
* Diffusion of science reports and entertainment news represent both, anti-persistent and persistence processes.
* Random texts represent processes with no dependencies, or a random walk, corresponding to stochastic processes defined by white noise.