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TemporalWord Embeddings for Early Detection of Signs of Depression

Depression is one of the most debilitating mental health diseases. Detecting the presence of depressive symptoms in the early stages of the disease is essential to reduce further consequences. As the study of language and behaviour is a pivotal component in mental research, social network content positions itself as a helpful tool. This paper introduces a general framework to analyze variations in the individual’s use of language over time on social media. We present a novel approach using temporal word representations to quantify the magnitude of words movements. This framework allows us to evaluate if words evolution can reveal the presence of depressive tendencies. We adapted different temporal word embedding representations to our framework and assessed them in Reddit benchmark datasets. Our results achieve high competitiveness compared with state-of-the-art methods, showing the potential that time-aware word representation models can bring to early detection scenarios.