EXPLAINABLE DETECTION OF DEPRESSION IN SOCIAL MEDIA CONTENTS USING NATURAL LANGUAGE PROCESSING

Abstract

Depression is a prevalent mental health condition that significantly impacts individuals’ lives, and its timely detection is crucial for effective intervention. Traditional machine learning approaches often struggle due to the limitations in annotated data and the lack of transparency in model predictions. This study aims to address these challenges by employing advanced natural language processing (NLP) techniques and deep learning algorithms, specifically Long Short-Term Memory (LSTM) networks, to develop an explainable model for depression detection in social media content. The primary objective is to classify social media text into two categories: depression and control, based on linguistic patterns indicative of depressive symptoms. The model leverages LSTM to capture the sequential dependencies in text, making it capable of identifying nuanced patterns that distinguish between depression-related and non-depression-related content. Additionally, the study incorporates interpretability methods such as attention mechanisms to provide insights into the features influencing the model's predictions, thus ensuring transparency and trust in the decision-making process. The proposed model is evaluated using a publicly available Mental Health dataset, which contains labeled social media posts. The results demonstrate the effectiveness of LSTM in classifying text into depression and control categories, contributing to the field of mental health by offering a scalable and interpretable approach for early depression detection. This research has the potential to assist mental health professionals by enabling the automated identification of depression in social media content, facilitating timely intervention and improving overall wellbeing. Index Terms – Depression detection, Natural Language Processing (NLP), Long ShortTerm Memory (LSTM), social media analysis, deep learning, mental health analytics, text classification, explainable artificial intelligence (XAI), attention mechanism, sentiment analysis, mental health prediction, healthcare AI.