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Thin Slices of Depression: Improving Depression Detection Performance Through Data Segmentation

The computing community is making major efforts towards automatic detection of depression, a serious pathology that affects roughly 4.4% of the world’s population. One of the main difficulties is the collection of data aimed at training models capable to learn differences between depressed and non-depressed people. In fact, data collection in the depression domain requires the respect of rigorous ethical constraints that, inevitably, limit the size of the corpora that can be collected. This article proposes to address the problem by using the thin slices theory, i.e., the possibility to detect the inner state of an individual (depression in this case) through very short samples of behavior. In particular, the article shows that the performance of data-driven models can be improved by segmenting the data at disposition into thin slices and then training data-driven models over them. This increases the amount of samples at disposition and allows a relative F1 Score improvement by up to 16.2%.