Mental Health Risk Detection With Artificial Intelligence
ISEF · 2022 Behavioral and Social Sciences
Overview
Mental health challenges, especially depression, are a major issue that impacts people globally. Combined with the prevalent use of social media, this issue is amplified and needs to be mitigated. With advancements in artificial intelligence, this project leverages social media’s pervasiveness as a platform to detect depression risks. It addresses the question if depression can be accurately detected on social media. Previous research focused primarily on machine learning techniques but oftentimes are overly complicated and, more importantly, lack perspectives from behavioral science. To overcome these limitations, this project focused on careful data selection and preparation and development of a measurement system to detect depression and evaluate effectiveness. Data from a peer-reviewed conference was processed through a linguistic processing tool to extract features from social media texts, and different variables were tested to determine their impact on detection accuracy. Three machine learning models are used for detection, and the results are evaluated based on confusion matrix criteria. The results concluded that overall accuracy of over 80% was attainable and consistent across different amounts of data, model types, and the number of features. For this sample, the RFC (Random Forest Classifier) and CNN (Convolutional Neural Network Classifier) models detected or predicted the most depressed users (=33,000 of 100,000 depressed users) within 2 seconds of compute time. This supports the hypothesis that social media can be a platform for depression detection, and potentially improve the lives of thousands of undiagnosed people. It is a cost-effective and accessible method to identify at-risk users hidden in plain sight.
Competition history
- ISEF 2022
Resources
Related projects
ISEF · 2025
Social Media's Impact on University Students' Mental Health: An AI Prediction Model
ISEF · 2022
SuiSensor: A Novel, Low-Cost Machine Learning System for Real-Time Suicide Risk Identification and Treatment Optimization via Computational Linguistics
ISEF · 2019
iSense: Artificial Intelligence Based Early Detection Tool to Identify Linguistic Bio-Markers of Mood Disorders and Recognize At-Risk Individuals
ISEF · 2022
Predicting Onset of Depressive Disorder Using Machine Learning
Closest projects by meaning, across every fair and year in the corpus.
Source: Regeneron International Science and Engineering Fair