Understanding reminiscence and its negative functions in the everyday conversations of young adults: A machine learning approach

Reminiscence is the act of recalling or telling others about relevant personal past experiences. It is an important activity for all individuals, young and old alike. In fact, reminiscence can serve different functions that can support or be detrimental to one's well-being. Although previous studies have extensively investigated older adults' recalling of autobiographical memories, the evidence for young adults remains scarce. Therefore, in this work, we analyze young adults' production of reminiscence and their functions with a naturalistic observation method. Furthermore, we demonstrate that natural language processing and machine learning can automatically detect reminiscence and its negative functions in young adults' everyday conversations. We interpret machine learning model results using Shapley explanations. Our results indicate that young adults reminisce in everyday life mostly to connect with others through conversation, to compensate for a lack of stimulation or to recall difficult past experiences. Moreover, our models improve existing benchmarks from the literature on the automated detection of older adults' reminiscence in everyday life. Finally, our results may support the development of digital health intervention programs that detect reminiscence and its functions in young adults to support their well-being.