Landslide research chiefly relies on digital inventories for a multitude of spatial, temporal, and/or process analyses. In respect thereof, many landslide inventories are populated with information from textual documents (e.g., news articles, technical reports) due to effectiveness. However, information detail can vary greatly in these documents and the question arises whether such textual information is suitable for landslide inventories. The present work proposes to define the usefulness of textual source types as a probability to find landslide information, weighted with adaptable parameter requirements. To illustrate the method with practical results, a German landslide dataset has been examined. It was found that three combined source types (administrative documents, expert opinions, and news articles) give an 89 % chance to detect useful information on three defined parameters (location, date, and process type). In conclusion, the definition of usefulness as a probability makes it an intuitive, quantitative measure that is suitable for a wide range of applicants. Furthermore, a priori knowledge of usefulness allows for focusing on a few source types with the most promising outcome and thus increases the effectiveness of textual data acquisition and digitalisation for landslide inventories.