The rise of large language models (LLMs) has revolutionized natural language processing (NLP), creating opportunities to address complex societal challenges, including the pervasive issue of harmful online content. Despite global regulations and platform-specific policies, abusive speech and toxic content continue to plague digital spaces, highlighting the need for smarter, scalable, and multilingual solutions.

This talk explores how modern NLP technologies can play a transformative role in content moderation, moving beyond traditional detection methods to proactive measures that promote healthier online interactions. We will cover key topics, including:

• Understanding the Landscape: Definitions and nuances of harmful content categories, including hate speech, misinformation, and harassment. We will bring practices not only from CS field, but from communication with social scientists and NGOs.
• Hate Speech Detection: Can LLMs detect hate speech? How the models can be adapted to new languages?
• Text Detoxification: Diving into nuances of toxicity of 9 languages (from our recent shared task) and sharing best practice on LLMs prompting for texts detoxification.
• Counter-Speech Generation: Our recent research results on how make LLMs generate not a very general "Please, it is not ok to talk like this report" but indeed address the targeted group.
• Ethical Considerations: Who, in the end, responsible for the content moderation? How the community can help to bring best practices? How the measure the "effectiveness" of LLMs for content moderation?