Decoding the Mind of Self-Compassion: A Topic Modeling Analysis of 9,000+ Free-Text Narratives

Self‑compassion, defined as compassion directed toward oneself in difficult situations, has been widely studied. However, the specific cognitive and behavioral patterns related to self-compassion remain poorly understood. Previous research that relies on predefined rating scales restricts the discovery of novel processes, whereas qualitative analyses of small free‑text samples suffer from limited generalizability. To address these limitations, we applied structural topic modeling—a natural language processing technique—to 9,360 free‑text responses (12 responses each from 780 participants; mean age = 43.0, SD = 10.6, range = 19–75) to identify thought and behavior patterns associated with self‑compassion. Participants responded to 12 free‑text prompts asking them to describe their typical thoughts and behaviors in three difficult situations (suffering, recognizing personal shortcomings, and experiencing failure). Higher self‑compassion was linked to topics reflecting problem‑solving orientation, balanced optimism, and adaptive flexibility. In contrast, lower self‑compassion was associated with self‑criticism, upward social comparison, envy, and depressive inaction. These patterns varied by context: for example, among individuals high in self‑compassion, balanced optimism predominated in contexts of suffering and failure, while adaptive flexibility emerged when participants recognized personal shortcomings. This study advances the literature by offering context‑sensitive, nuanced insights into self‑compassion across situations and providing a data‑driven foundation for future theoretical models and interventions. It also demonstrates that a data‑driven approach employing topic modeling on large‑scale free‑text data can uncover nuanced processes that conventional rating scales may not capture, with broad implications for emotion research and psychological science.