  Teenager detection is an important case of the age detection task in social
media, which aims to detect teenage users to protect them from negative
influences. The teenager detection task suffers from the scarcity of labelled
data, which exacerbates the ability to perform well across social media
platforms. To further research in teenager detection in settings where no
labelled data is available for a platform, we propose a novel cross-platform
framework based on Adversarial BERT. Our framework can operate with a limited
amount of labelled instances from the source platform and with no labelled data
from the target platform, transferring knowledge from the source to the target
social media. We experiment on four publicly available datasets, obtaining
results demonstrating that our framework can significantly improve over
competitive baseline models on the cross-platform teenager detection task.
