However, as far as I know, there is not much research dedicated to the question, how much the loss of information caused by DTFs affects the TDM results. <hi xmlns="http://www.tei-c.org/ns/1.0" rend="Quote_Char">Eder (2013)</hi> presented an empirical study of verifying the impact of unwanted noise in texts on authorship attribution and emphasizing that the usefulness of damaged texts should not be underestimated in stylometric studies. He brought noise into texts by (a) randomly replacing a portion of characters, (b) increasing standard deviation of word counts and (c) randomly replacing original words with other words in the same corpus, in order to show the correlation between a dirty corpus and the attribution accuracy. The presented paper did a similar empirical study by transforming texts into token-based DTFs and provide a review on the correlation between information loss caused by these DTFs and the loss of accuracy in authorship classification.