Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations

Understand

A limited amount of studies investigates the role of model-agnostic adversarial behavior in toxic content classification.

• As toxicity classifiers predominantly rely on lexical cues, (deliberately) creative and evolving language-use can be detrimental to the utility of current corpora and state-of-the-art models when they are deployed for content moderation.
• The less training data is available, the more vulnerable models might become.
• This study is, to our knowledge, the first to investigate the effect of adversarial behavior and augmentation for cyberbullying detection.