A neural network approach to early risk detection of depression and anorexia on social media text
Journal
CEUR
Journal Volume
2125
Date Issued
2018
Author(s)
Abstract
In recent years, people actively write text messages on social media platforms like Twitter and Reddit. The text shared on social media drives various applications including influenza detection, suicide detection, and mental illness detection. This work presents our approach to early risk detection of depression and anorexia on social media in CLEF eRisk 2018. For the two mental illnesses, our models combine TF-IDF information and convolutional neural networks (CNNs) to identify the articles written by potential patients. The official evaluation shows ourmodels achieve ERDE5 of 10.81%, ERDE50 of 9.22%, andF-score of 0.37 in depression detection and ERDE5 of 13.65%, ERDE50 of 11.14%, and F-score of 0.67 in anorexia detection.
Subjects
Anorexia
Convolutional Neural Network
Depression
Early Risk Detection
SDGs
Other Subjects
Convolution; Diseases; Neural networks; Anorexia; Convolutional neural network; Depression; F-score; Mental illness; Risk detections; Social media; Social media platforms; Social networking (online)
Type
conference paper