  The increasing adoption of connectivity and electronic components in vehicles
makes these systems valuable targets for attackers. While automotive vendors
prioritize safety, there remains a critical need for comprehensive assessment
and analysis of cyber risks. In this context, this paper proposes a Social
Media Automotive Threat Intelligence (SOCMATI) framework, specifically designed
for the emerging field of automotive cybersecurity. The framework leverages
advanced intelligence techniques and machine learning models to extract
valuable insights from social media. Four use cases illustrate the framework's
potential by demonstrating how it can significantly enhance threat assessment
procedures within the automotive industry.
