Citizen science to approach machine learning to society: Detecting loneliness in older adults

Background Even if we are not aware of it, machine learning techniques are part of our daily lives. It is of the utmost interest that citizens become familiar with the use of these techniques and discover their potential to solve everyday problems. Objective and Methods In this article, we describe the methodology and results of a highly replicable citizen science project that allows citizens to get closer to the scientific process and understand the potential of machine learning to solve a social problem of interest to them. For this purpose, we have chosen a problem of social relevance in contemporary societies, namely the detection of loneliness in older adults. Citizens are challenged to apply machine learning techniques to identify levels of loneliness from natural language. Results The results of this project suggest that citizens are willing to engage in science when the challenges posed are of social interest to them. A total of 1517 citizens actively engaged in the project. A database containing 1112 texts about loneliness expressions was collected. An accuracy of 83.12% using the logistic regression algorithm and 62.23% accuracy when using the Naïve Bayes algorithm was reached in detecting loneliness from texts. Conclusions Detecting loneliness using machine learning techniques is an attractive and relevant topic that allows citizens to be involved in science and introduces them to machine learning practices. The methodology of this project can be replicated in other places around the world.