To address the negative impacts of toxic online behavior by developing a multi-agent system that can identify and counter harmful content.
Problem Space
The internet is increasingly populated by bots and trolls that spread misinformation and engage in harassment. This has a negative impact on online discourse and can lead to real-world harm.
Proposed Solution
We propose to build a multi-agent system that can:
Identify harmful content: Use natural language processing (NLP) to identify toxic language, hate speech, and misinformation.
Counter harmful content: Generate counter-narratives and engage with users in a positive and constructive way.
Promote healthy online communities: Encourage positive online behavior and create a more welcoming and inclusive online environment.
Tech Stack
Large Language Models: Llama 3 70B, Mixtral 8B
Fine-tuning: Fine-tune a smaller model on a corpus of Reddit data to identify and classify harmful content.
Agent Framework: Use the Agentic Alliance tech stack to build and deploy the multi-agent system.
Data Sources: Reddit API (if available), other social media platforms.
Challenges
Data availability: Access to social media data is becoming increasingly restricted.
Defining “harmful content”: What constitutes harmful content is subjective and can vary depending on the context.
Ethical considerations: It is important to ensure that the multi-agent system is used in a responsible and ethical way.