Interrogative insights into depression detection via social networks and machine learning techniques

Abstract

As users on social networks (SNs) interact with one another by exchanging information, giving feedback, finding new content, and participating in discussions; thus, generating large volumes of data each day. This data includes images, texts, videos and can be used to help the user find out how they have been doing, when they were depressed, how not to be depressed, and other similar insights. Depression is one of the most common chronic illnesses and it has emerged as a global mental health problem. But the lack of these data is incomplete, sparse and sometimes inaccurate, and so the task of diagnosing depression using automated systems is still proving a challenge. Various techniques have been used to detect depression through the years however, machine learning (ML) and deep learning (DL) techniques offer better ways. In the context of that, this study reviews state-of-the-art ML and DL approaches for the detection of depression using systematic literature review (SLR) method as well as highlight fundamental challenges in literature, which future works can focus on. We hope that this survey will provide a better understanding of these strategies for the readers and researchers in the ML and DL fields, when it comes to diagnosis of depression.