  Understanding the vulnerability of linguistic features extracted from noisy
text is important for both developing better health text classification models
and for interpreting vulnerabilities of natural language models. In this paper,
we investigate how generic language characteristics, such as syntax or the
lexicon, are impacted by artificial text alterations. The vulnerability of
features is analysed from two perspectives: (1) the level of feature value
change, and (2) the level of change of feature predictive power as a result of
text modifications. We show that lexical features are more sensitive to text
modifications than syntactic ones. However, we also demonstrate that these
smaller changes of syntactic features have a stronger influence on
classification performance downstream, compared to the impact of changes to
lexical features. Results are validated across three datasets representing
different text-classification tasks, with different levels of lexical and
syntactic complexity of both conversational and written language.
