Emotions have attracted a lot of attention in psychology, socio- and psycholinguistics and communication science, but since the past decade also in the fields of computational linguistics and natural language processing. In the latter fields, the term emotion detection is used to refer to the task of automatically identifying fine-grained emotions in texts. Research on emotion detection has mainly focused on English, but with the emergence of huge language models like mBERT and technologies like transfer learning, the interest in multilingual approaches to emotion detection increased. Meanwhile, state-of-the-art research in psychology have developed new theories about emotion, claiming that emotions are not universal: neither in conceptualisation, nor in emotion expression. This might have consequences for how multilingual emotion detection models work. Therefore, the goal of this research project is to examine the differences in emotional language use across languages and to investigate how state-of-the-art emotion detection models deal with cross-lingual differences in emotion verbalisation.