Lightweight NSM: A clinical-linguistic approach to narcissism detection on social media

The rise of social media has brought renewed attention to the study of personality traits like narcissism. While much research has focused on platforms like Facebook and Instagram, decentralized networks such as Mastodon remain relatively unexplored. This paper introduces Lightweight NSM, a text-based model for detecting narcissistic traits in social media posts, grounded in the clinical criteria of the DSM-5. We collected and annotated a dataset of 2,000 Mastodon posts using GPT-4.1-mini, finding that a threshold of two or more DSM-5 criteria provided a reasonable baseline for identifying potentially narcissistic content. Our model, a simple Logistic Regression classifier using TF-IDF features, was trained on this annotated dataset. The model achieved an F1-score of 0.69 for the narcissistic class. Our analysis revealed a notable difference in narcissistic content between posts tagged with #selfie (29.1%) and #selfportrait (3.9%), representing a 7.5-fold difference. This work offers a reproducible pipeline for studying personality traits on emerging platforms and highlights the potential of using clinical frameworks in computational social science.