Comprehensive Examination of Depression Detection Through Multimedia Content on Social Media Platforms

Depression is a mental health disorder that causes persistent feelings of sadness, hopelessness, and a loss of interest or pleasure in activities that one used to enjoy. It can be caused by a combination of genetic, biological, environmental, and psychological factors. Identifying depression causes...

Full description

Saved in:
Bibliographic Details
Published in2025 International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI) pp. 221 - 226
Main Authors Walia, Chayan Preet, Maheshwary, Priti
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.01.2025
Subjects
Analytical models
Biological system modeling
Computational modeling
Data models
deep learning
Depression
depression detection
image recognition
machine learning
Mental health
Natural language processing
Reviews
sentimental analysis
social media
Social networking (online)
User-generated content
Online AccessGet full text

Cover

More Information
Summary:Depression is a mental health disorder that causes persistent feelings of sadness, hopelessness, and a loss of interest or pleasure in activities that one used to enjoy. It can be caused by a combination of genetic, biological, environmental, and psychological factors. Identifying depression causes in social media involves the use of data mining and natural language processing techniques to analyze user-generated content and behavior. Depression analysis is a popular research topic in the natural language processing field, and researchers are increasingly publishing papers on this subject. This review paper aims to explore the use of machine learning techniques for depression analysis using social media text, audio, and video data. The paper provides an overview of the different machine learning techniques used for depression analysis and their effectiveness. The review paper can help researchers understand the current state of knowledge on depression analysis using social media text, images, and videos along with machine learning techniques and identify potential areas for future research.
DOI:10.1109/IC3ECSBHI63591.2025.10990523