Early detection of depression based on linguistic metadata augmented classifiers revisited: Best of the erisk lab submission

Abstract

Early detection of depression based on written texts has become an important research area due to the rise of social media platforms and because many affected individuals are still not treated. During the eRisk task for early detection of depression at CLEF 2017, FHDO Biomedical Computer Science Group (BCSG) submitted results based on five text classification models. This paper builds upon this work to examine the task and especially the ERDEo metric in further detail and to analyze how an additional type of metadata features can help in this task. Finally, different prediction thresholds and ensembles of the developed models are utilized to investigate the possible improvements, and a newly proposed alternative early detection metric is evaluated.