The automatic identification of harmful content online is of major concern for social media
platforms, policymakers, and society. Researchers have studied textual, visual, and audio …

Large language models (LLMs) have strong capabilities in solving diverse natural language
processing tasks. However, the safety and security issues of LLM systems have become the …

We introduce a generic, language-independent method to collect a large percentage of
offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic …

Counterfactually Augmented Data (CAD) aims to improve out-of-domain generalizability, an
indicator of model robustness. The improvement is credited with promoting core features of …

Cyberbullying on social media continues to pose a significant problem for children and
young people, and platforms increasingly rely on artificial intelligence (AI) to address it …

As NLP models are increasingly deployed in socially situated settings such as online
abusive content detection, it is crucial to ensure that these models are robust. One way of …

Robustness of machine learning models on ever-changing real-world data is critical,
especially for applications affecting human well-being such as content moderation. New …

Online harassment in the form of hate speech has been on the rise in recent years.
Addressing the issue requires a combination of content moderation by people, aided by …

With the growth of social media platform influence, the effect of their misuse becomes more
and more impactful. The importance of automatic detection of threatening and abusive …

Cyberbullying is the wilful and repeated infliction of harm on an individual using the Internet
and digital technologies. Similar to face-to-face bullying, cyberbullying can be captured …