MentalSpot: Effective Early Screening for Depression Based on Social Contagion

While depression is rated as the most important leading factor to global disability, early detection of depression is a non-trivial task. Existing depression detection mechanisms harvesting social media data suffer from two major limitations. First, existing solutions rely heavily on the amount, quality, and variety of content types (textual, visual, etc.) posted by users to make accurate inferences, therefore suffering from the cold-start problem when coping with users with limited training data (e.g., most existing works exclude users with fewer than 25 tweets). Second, existing approaches ignore the social impact or indication from users' social circles that can be leveraged to enhance the inference results. In this paper, we present MentalSpot, a social-contagion based depression early-screening framework using meta-learning. Specifically, we first construct a social-contagion driven data repository PsycheNet, filling the void of social-circle based depression datasets. We design a triplet network to extract users' embeddings based on the similarities of the linguistic features extracted from written texts. Afterwards, for each target user, we employ dynamic mean shift pruning to select her top-k homogeneous friends in the metric space, the texts written by whom will then be leveraged to train a friend based depression detection model. Extensive experiments show that MentalSpot outperforms the state of the art in terms of all effectiveness metrics, especially for users with very few tweets. Specifically, by using only five tweets per user, MentalSpot successfully yields an F1 score that would otherwise be achieved by the state-of-the-art methods requiring at least twenty tweets. Our approach represents a step forward to address the cold-start problem that deep learning techniques struggle with for their applications in psychiatric diagnosis. The principal beneficiaries of this study are healthcare professionals in medical institutions to determine timely and targeted interventions in a clinical setting. This study also supports non-profit groups in reaching out to people with mental health issues, helping in a global health task that cannot be fully covered by clinicians.