As such, DEviaNT is at a disadvantage to many potential solutions because DE-viaNT does not aggressively explore features specific to TWSSs (e.g., DEviaNT does not use a lexical n-gram model of the TWSS training data).
the goal of our evaluation is not to outperform the baselines in all aspects, but rather to show that by using only euphemism-based and structure-based features, DEviaNT can compete with the baselines, particularly where it matters most, delivering high precision and few false positives.