This paper provides an exploration of the usefulness of three token-based DTFs for frequency-based authorship classification with Delta. As presented, selectively reducing information on individual tokens could ensure, to a certain extent, that the authorship classification results are not affected too much. The impact of token-based DTFs on the results of Delta test can be reduced by considering only replacing or removing content words, while all function words remain unchanged. But this limits the application of the texts on other TDM tasks such as topic modeling. For the future work, a series of tests are planned on evaluating the usefulness of token-based DTFs on other TDM tasks. The goal is to find DTFs that could balance various factors (e.g. word frequency, sequence information, content vs. function words, copyright) so that texts could be published and used for as many TDM tasks as possible without violating copyright law.