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Depression places substantial pressure on mental health services, and many people describe their experiences outside clinical settings in high-volume user-generated text (e.g., online forums and social media). Automatically identifying…

Mental disorders such as depression and suicidal ideation are hazardous, affecting more than 300 million people over the world. However, on social media, mental disorder symptoms can be observed, and automated approaches are increasingly…

We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We…

Depression is the most common mental health disorder, and its prevalence increased during the COVID-19 pandemic. As one of the most extensively researched psychological conditions, recent research has increasingly focused on leveraging…

This work explores the utilization of Romanized Sinhala social media data to identify individuals at risk of depression. A machine learning-based framework is presented for the automatic screening of depression symptoms by analyzing…

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not…

Depression is a globally prevalent mental disorder with potentially severe repercussions if not addressed, especially in individuals with recurrent episodes. Prior research has shown that early intervention has the potential to mitigate or…

Depression is a common disease worldwide. It is difficult to diagnose and continues to be underdiagnosed. Because depressed patients constantly share their symptoms, major life events, and treatments on social media, researchers are turning…

In this paper we present our approach for detecting signs of depression from social media text. Our model relies on word unigrams, part-of-speech tags, readabilitiy measures and the use of first, second or third person and the number of…

Mental health disorders may cause severe consequences on all the countries' economies and health. For example, the impacts of the COVID-19 pandemic, such as isolation and travel ban, can make us feel depressed. Identifying early signs of…

Early detection of depression from social media data offers a valuable opportunity for timely intervention. However, this task poses significant challenges, requiring both professional medical knowledge and the development of accurate and…

This study investigates the utility of speech signals for AI-based depression screening across varied interaction scenarios, including psychiatric interviews, chatbot conversations, and text readings. Participants include depressed patients…

We take interest in the early assessment of risk for depression in social media users. We focus on the eRisk 2018 dataset, which represents users as a sequence of their written online contributions. We implement four RNN-based systems to…

The COVID-19 pandemic has escalated mental health crises worldwide, with social isolation and economic instability contributing to a rise in suicidal behavior. Suicide can result from social factors such as shame, abuse, abandonment, and…

Depression significantly affects emotions, thoughts, and daily activities. Recent research indicates that speech signals contain vital cues about depression, sparking interest in audio-based deep-learning methods for estimating its…

Compared to physical health, population mental health measurement in the U.S. is very coarse-grained. Currently, in the largest population surveys, such as those carried out by the Centers for Disease Control or Gallup, mental health is…

This paper describes our participation in the MentalRiskES task at IberLEF 2023. The task involved predicting the likelihood of an individual experiencing depression based on their social media activity. The dataset consisted of…

Research exploring linguistic markers in individuals with depression has demonstrated that language usage can serve as an indicator of mental health. This study investigates the impact of discussion topic as context on linguistic markers…

Amid lockdown period more people express their feelings over social media platforms due to closed third-place and academic researchers have witnessed strong associations between the mental healthcare and social media posts. The stress for a…

Depression is the leading cause of disability worldwide. Initial efforts to detect depression signals from social media posts have shown promising results. Given the high internal validity, results from such analyses are potentially…