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Since the advent of online social media platforms such as Twitter and Facebook, useful health-related studies have been conducted using the information posted by online participants. Personal health-related issues such as mental health,…

This study investigates the use of Large Language Models (LLMs) for improved depression detection from users social media data. Through the use of fine-tuned GPT 3.5 Turbo 1106 and LLaMA2-7B models and a sizable dataset from earlier…

Depression is a common mental health issue that requires prompt diagnosis and treatment. Despite the promise of social media data for depression detection, the opacity of employed deep learning models hinders interpretability and raises…

The COVID-19 pandemic has forced many people to limit their social activities, which has resulted in a rise in mental illnesses, particularly depression. To diagnose these illnesses with accuracy and speed, and prevent severe outcomes such…

Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications…

Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple reviews have been published on this topic, providing the community with comprehensive accounts of…

Motivated with recent advances in inferring users' mental state in social media posts, we identify and formulate the problem of finding causal indicators behind mental illness in self-reported text. In the past, we witness the presence of…

Suicide is a prominent issue in society. Unfortunately, many people at risk for suicide do not receive the support required. Barriers to people receiving support include social stigma and lack of access to mental health care. With the…

Human content moderators (CMs) routinely review distressing digital content at scale. Beyond exposure, the work context (e.g., workload, team structure, and support) may shape mental health outcomes. We examined a cross sectional…

In recent years, we have seen deep learning and distributed representations of words and sentences make impact on a number of natural language processing tasks, such as similarity, entailment and sentiment analysis. Here we introduce a new…

Depression is one of the leading causes of disability worldwide, posing a severe burden on individuals, healthcare systems, and society at large. Recent advancements in Large Language Models (LLMs) have shown promise in addressing mental…

The new coronavirus outbreak has been officially declared a global pandemic by the World Health Organization. To grapple with the rapid spread of this ongoing pandemic, most countries have banned indoor and outdoor gatherings and ordered…

Social isolation (SI) in older adults has emerged as a critical mental health concern, with established links to cognitive decline. While depression is hypothesized to mediate this relationship, the longitudinal mechanisms remain unclear.…

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…

Mental disorders pose a global challenge, aggravated by the shortage of qualified mental health professionals. Mental disorder prediction from social media posts by current LLMs is challenging due to the complexities of sequential text data…

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…

Accurate and interpretable detection of depressive language in social media is useful for early interventions of mental health conditions, and has important implications for both clinical practice and broader public health efforts. In this…

Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction. However, most of the existing machine learning methods provide no interpretability for depression…

Autistic individuals often experience negative self-talk (NST), leading to increased anxiety and depression. While therapy is recommended, it presents challenges for many autistic individuals. Meanwhile, a growing number are turning to…

Depression is a mental health disorder that has a profound impact on people's lives. Recent research suggests that signs of depression can be detected in the way individuals communicate, both through spoken words and written texts. In…