This research evaluates the effectiveness of machine learning, deep learning, and large language models in detecting cyber threats across multiple languages on Twitter/X.

Key Findings:

• Demonstrates methodologies for identifying disguised threats in social media content
• Compares performance of traditional ML, deep learning, and LLM approaches
• Extends threat detection capabilities beyond English to multiple languages
• Provides insights for building more effective cross-lingual security monitoring systems

Business Impact: Organizations can leverage these approaches to enhance their social media monitoring capabilities, protect against reputational damage, and identify emerging threats that may be discussed in multiple languages before they spread widely.

Multi-Lingual Cyber Threat Detection in Tweets/X Using ML, DL, and LLM: A Comparative Analysis