What can we learn from the past about the future of gerontology: Using natural language processing to examine the field of gerontology.

OBJECTIVES: We thematically classified all titles of eight top psychological and social gerontology journals over a period of six decades, between 1961 and February, 2020. This was done in order to provide a broad overview of the main topics that interest the scientific community over time and place.

METHODS: We used natural language processing in order to analyze the data. In order to capture the diverse thematic clusters covered by the journals, a cluster analysis, based on "topic detection" was conducted.

RESULTS: A total of 15,566 titles were classified into 38 thematic clusters. These clusters were then compared over time and geographic location. The majority of titles fell into a relatively small number of thematic clusters and a large number of thematic clusters was hardly addressed. The most frequently addressed thematic clusters were: a) Cognitive functioning, b) Long term care and formal care, c) Emotional and personality functioning, d) health and e) Family and informal care. The least frequently addressed thematic clusters were: a) Volunteering, b) Sleep, c) Addictions, d) Suicide, and e) Nutrition. There was limited variability over time and place with regard to the most frequently addressed themes.