The rise of social media has transformed tourism research, providing new ways to understand travelers’ perspectives. Picture analysis, in particular, offers valuable insights into tourist preferences, popular attractions, and emotions conveyed through images. This analysis can be performed manually or with artificial intelligence. However, a significant challenge arises from the presence of memes and advertisements related to informal markets, which complicate data usability. Manually filtering such content is labor-intensive and inefficient. To address this, we propose a robust analytical methodology that combines traditional and modern learning techniques. Our approach achieves over 89% accuracy in its classification task, streamlining data processing for tourism research. By automating image filtering, this method enhances dataset quality and improves the reliability of tourism analyses.