  Content moderation is the process of screening and monitoring user-generated
content online. It plays a crucial role in stopping content resulting from
unacceptable behaviors such as hate speech, harassment, violence against
specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to
mention some few, in Online Social Platforms. These platforms make use of a
plethora of tools to detect and manage malicious information; however,
malicious actors also improve their skills, developing strategies to surpass
these barriers and continuing to spread misleading information. Twisting and
camouflaging keywords are among the most used techniques to evade platform
content moderation systems. In response to this recent ongoing issue, this
paper presents an innovative approach to address this linguistic trend in
social networks through the simulation of different content evasion techniques
and a multilingual Transformer model for content evasion detection. In this
way, we share with the rest of the scientific community a multilingual public
tool, named "pyleetspeak" to generate/simulate in a customizable way the
phenomenon of content evasion through automatic word camouflage and a
multilingual Named-Entity Recognition (NER) Transformer-based model tuned for
its recognition and detection. The multilingual NER model is evaluated in
different textual scenarios, detecting different types and mixtures of
camouflage techniques, achieving an overall weighted F1 score of 0.8795. This
article contributes significantly to countering malicious information by
developing multilingual tools to simulate and detect new methods of evasion of
content on social networks, making the fight against information disorders more
effective.
