Whonix does not obfuscate a user's writing style. Consequently, unless precautions are taken (see below), users are at risk from stylometric analysis based on their linguistic style. Research suggests only a few thousand words (or less) may be enough to positively identify an author and there are a host of software tools available to conduct this analysis.
This technique is used by advanced adversaries to attribute authorship to anonymous documents, online texts (web pages, blogs etc.), electronic messages (emails, tweets, posts etc.) and more. The field is dominated by A.I. techniques like neural networks and statistical pattern recognition, and is critical to privacy and security. Current anonymity and circumvention systems are focused on location-based privacy, but ignore leakage of identification via the content of data which has a high accuracy in authorship recognition (90%+ probability). 
- Stylistic flourishes.
- Spelling preferences and misspellings.
- Language preferences.
- Word frequency.
- Number of unique words.
- Regional linguistic preferences in slang, idioms and so on.
- Sentence/phrasing patterns.
- Word co-location (pairs).
- Use of formal/informal language.
- Function words.
- Vocabulary usage and lexical density.
- Character count with white space.
- Average sentence length.
- Average syllables per word.
- Synonym choice.
- Expressive elements like colors, layout, fonts, graphics, emoticons and so on.
- Analysis of grammatical structure and syntax.
Fortunately research suggests that if users purposefully obfuscate their linguistic style or imitate the style of other known authors, this is largely successful in defeating all stylometric analysis methods so they are no better than randomly guessing the correct author of a document. However, using automated methods like machine translation services do not appear to be a viable method of circumvention.