Academy & Industry Research Collaboration Center (AIRCC)

Volume 12, Number 12, July 2022

Detecting Depression in Social Media using Machine Learning


Ruoxi Ding1 and Yu Sun2, 1USA, 2California State Polytechnic University, USA


Social Media Depression Detection is an Intelligent System to automate the detection of Youth Depression with social media (Instagram) using AI and Deep Learning. The student is the targeted group because most students with depression express themselves on social media rather than seeking help from doctors. This app gathers captions and images from the user's personal Instagram profile through web scraping using Instagram private API to check whether or not the posts are depressive. The google cloud dataset supports the captions and pictures analysis performed by the app [6]. Caption sentiment analysis depends on sentiment analysis, and the pictures analysis depends on classifying images by custom labels. The app reports the image and the caption analysis results back to the user. Python is used for the back-end functionality, while Dart and Flutter are used for the front-end. It was tested by 2 experiments, the first experiments returned the feedback of 15 students demonstrates that the program has the capability of detecting depression through the captions with relatively high accuracy. The second experiment of testing the app functionality on the same account demonstrates that the program is stable and consistent. The purpose of the app is to detect depression at an early stage to prevent the condition from worsening.


NLP, Web Scraping, Machine Learning.