D epress M ind: A Depression Surveillance System for Social Media Analysis (2024)

Abstract: 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).

## introduction psychological analysis is a multifaceted discipline dedicated to the study of human behavior and the intricacies of related mental processes. this field holds immense significance as it helps to gain deeper insights into individuals and the functioning of their minds. such insights are instrumental in both the treatment and prevention of mental health issues. among the most pressing concerns within psychological analysis is depression, a pervasive mood disorder that affects countless individuals worldwide @xcite . depression constitutes a serious affliction that significantly diminishes a person's quality of life and can have profound consequences on his physical and mental well-being. although there is a wide range of available therapeutic approaches, ranging from psychotherapy to pharmaceutical interventions, the burden of inadequately treated or unrecognized depression is still substantial @xcite . recent years have witnessed a noticeable increase in the global prevalence of depression @xcite , reflecting the need for further research and design of new screening tools. the internet and social media (sm) have sparked a revolutionary transformation in the way we communicate. online sources have also opened up new possibilities in psychological analysis, particularly for extracting behavioral patterns from specific demographic groups. one of the primary advantages is the availability of a large amount of data and multiple pieces of evidence about human behavior. these valuable resources can be exploited to perform observational studies that support mental health research. sm platforms are spaces where individuals can freely express themselves and publicly share their thoughts, feelings, and opinions. some popular sites and forums have become meeting points for disclosing personal preoccupations. notably, online communities focused on topics like depression and other mental health issues have emerged, enabling individuals to share their personal experiences and receive support from others who have navigated similar challenges @xcite @xcite . it is crucial to develop new screening mechanisms to early detect the increasing severity of depressive symptoms and provide explanatory extracts of the evidence found. to that end, we exploit advanced natural language processing (nlp) solutions to analyze patterns and trends of depression. such automated screening would allow to monitor a large number of people and identify those who may be at risk of developing a mental disorder. in practice, this could lead to interventions that provide individuals with the right support in time. alternatively, the insights gained by our tool could inform preventive measures and help detect risk factors. for example, by understanding the evolution of depressive symptoms in specific segments of the population (e.g. individuals of a certain age) we could instigate the definition of new communication or mitigation measures by relevant health authorities. in any case, this exploitation would have to be done with proper ethical and privacy precautions. the goal of this paper is to exemplify the potential contributions of this technology, while its actual deployment is beyond the scope of the present project. furthermore, depressmind is available for research purposes and, thus, those interested in extracting and analyzing online data from a bdi perspective can get access to this new screening tool foot_1 . depressmind stands on a retrieval method that supports a semantic representation for each category of the bdi-ii questionnaire. bdi @xcite is a recognized clinical instrument designed to assess the manifestation of 21 depressive symptoms, such as sadness, pessimism, or loss of energy. given a textual excerpt, depressmind computes two different metrics, relevance, and intensity, with respect to each bdi symptom. the whole process is depicted in figure 1 . we can summarize our contributions as follows: ## related work in recent times, the field of digital mental health analysis has experienced significant expansion. automated nlp techniques have emerged as effective means for identifying traces of mental health disorders from user-generated online publications. for instance, in a pioneering study, @xcite conducted initial research on automatically detecting depression. these researchers collected reliable data on depression through crowd-sourcing, employing the ces-d (center for epidemiological studies depression scale) inventory to assess depression levels @xcite . @xcite introduced an unsupervised method aimed at aligning the content from different mental health-related subreddits with the most suitable dsm-5 (diagnostic and statistical manual of mental disorders -5th edition) categories @xcite . given the knowledge encoded into the dsm-5 manual and other meticulously curated medical resources, a specialized lexicon was constructed. this language resource included n-grams associated with each mental health disorder listed in the dsm-5. additionally, the authors enhanced an existing ontology related to drug abuse, incorporating terminology and slang expressions derived from reddit. the resulting lexicon was exploited to quantify the connection between the content of the subreddits and the dsm-5 categories. in a study conducted by perez et al. @xcite , an innovative approach was introduced to automatically measure the severity of depression among social media users. this research team addressed the challenge of quantifying the intensity of depression indicators and explored the application of neural language models to capture various aspects of the user-generated content. this team presented two alternative methods for evaluating depression symptoms, based on the individual's willingness to openly discuss them. the first method relied on analyzing global language patterns present in the user's posts, while the second method focused on identifying direct mentions of symptom-related concerns. both approaches resulted in automatic estimates of the overall bdi-ii score. in a related vein, @xcite introduced a platform designed to assist in the assessment and monitoring of depression symptoms among social media users. the tool aimed at capturing a broad context of the individuals by incorporating user profiling capabilities. the authors claimed that the platform could assist professionals in labeling data and utilizing depression estimators and profiling models. these initiatives have piqued our interest in exploring the potential of semantic matching between relevant medical questionnaires and user-generated publications. our system, depressmind, allows us to extract and visualize bdi-related extracts and we present here a preliminary evaluation that shows its potential to automatically estimate an individual's response to the bdi-ii items. ## empirical evaluation to validate the tool, we utilized the erisk evaluation datasets (2019, 2020, and 2021) @xcite @xcite . these evaluation tasks aim to estimate the severity of the 21 bdi symptoms for a set of social media users. for each user, the datasets include a self-report bdi questionnaire and the user's history of publications (participants gave their consent for tracking of their public posts). we fed each user's collection to depressmind, obtained the estimated levels of severity, and compared them with those filled by the users. the next subsection describes the four effectiveness metrics. further details about them are available at @xcite @xcite . ## conclusion in this study, we try to contribute to the understanding of a pressing global issue: the prevalence of depression. our main goal was to make a positive contribution in the area of automated methods for screening depression. to that end, we introduced depressmind, an analytical tool that extracts social media posts and assesses their relevance and severity concerning the 21 standardized bdi symptoms. an evaluation of several depression datasets has produced promising results, highlighting the potential to enhance our understanding, assessment, and management of various depression symptoms. this work represents an initial advancement towards harnessing the power of data to address mental health challenges on a broader scale. in our future work, we aim to explore the use of specialized lexical resources for more effective sentence extraction and to exploit clinical data for training more specialized language models. we are also interested in expanding this study to different languages, as most of the research studies on mental disorders have been centered on english.