Depression is a global concern suffered by millions of people, significantly impacting their thoughts and behavior. Over the years, heightened awareness, spurred by health campaigns and other initiatives, has driven the study of this disorder using data collected from social media platforms. In our research, we aim to gauge the severity of symptoms related to depression among social media users. The ultimate goal is to estimate the user’s responses to a well-known standardized psychological questionnaire, the Beck Depression Inventory-II (BDI). This is a 21-question multiple-choice self-report inventory that covers multiple topics about how the subject has been feeling. Mining users’ social media interactions and understanding psychological states represents a challenging goal. To that end, we present here an approach based on search and summarization that extracts multiple BDI-biased summaries from the thread of users’ publications. We also leverage a robust large language model to estimate the potential answer for each BDI item. Our method involves several steps. First, we employ a search strategy based on sentence similarity to obtain pertinent extracts related to each topic in the BDI questionnaire. Next, we compile summaries of the content of these groups of extracts. Last, we exploit chatGPT to respond to the 21 BDI questions, using the summaries as contextual information in the prompt. Our model has undergone rigorous evaluation across various depression datasets, yielding encouraging results. The experimental report includes a comparison against an assessment done by expert humans and competes favorably with state-of-the-art methods.

Depression is a pressing global issue that impacts millions of individuals worldwide. This prevailing psychologicaldisorder profoundly influences the thoughts and behavior of those who suffer from it. We have developed DepressMind, a versatile screening tool designed to facilitate the analysis of social network data. This automated tool explores multiple psychological dimensions associated with clinical depression and estimates the extent to which these symptoms manifest in language use. Our project comprises two distinct components: one for data extraction and another one for analysis.The data extraction phase is dedicated to harvesting texts and the associated meta-information from social networks and transforming them into a user-friendly format that serves various analytical purposes.For the analysis, the main objective is to conduct an in-depth inspection of the user publications and establish connections between the posted contents and dimensions or traits defined by well-established clinical instruments.Specifically, we aim to associate extracts authored by individuals with symptoms or dimensions of the Beck Depression Inventory (BDI).

Automatic classification of behaviour change language can enhance conversational agents’ capabilities to adjust their behaviour based on users’ current situations and to encourage individuals to make positive changes. However, the lack of annotated language data of change-seekers hampers the performance of existing classifiers. In this study, we investigate the use of semi-supervised learning (SSL) to classify highly imbalanced texts around behaviour change. We assess the impact of including pseudo-labelled data from various sources and examine the balance between the amount of added pseudo-labelled data and the strictness of the inclusion criteria. Our findings indicate that while adding pseudo-labelled samples to the training data has limited classification impact, it does not significantly reduce performance regardless of the source of these new samples. This reinforces previous findings on the feasibility of applying classifiers trained on behaviour change language to diverse contexts.

Mental disorders affect millions of people worldwide and cause interference with their thinking and behavior. Through the past years, awareness created by health campaigns and other sources motivated the study of these disorders using information extracted from social media platforms. In this work, we aim to contribute to the study of these disorders and to the understanding of how mental problems reflect on social media. To achieve this goal, we propose a double-domain adaptation of a language model. First, we adapted the model to social media language, and then, we adapted it to the mental health domain. In both steps, we incorporated a lexical resource to guide the masking process of the language model and, therefore, to help it in paying more attention to words related to mental disorders. We have evaluated our model in the detection of signs of three major mental disorders: Anorexia, Self-harm, and Depression. Results are encouraging as they show that the proposed adaptation enhances the classification performance and yields competitive results against state-of-the-art methods.