Machine Learning Approaches for Suicidal Ideation Detection on Social Media

Title

Machine Learning Approaches for Suicidal Ideation Detection on Social Media

Creator

Nandhini B.; Jayanthi; Shrinivas K.; Vinod P.

Description

Social media suicidal ideation has become a serious public health issue that requires creative solutions for early diagnosis and management. An extensive investigation of machine-learning techniques for the automated detection of suicidal thoughts in internet postings is presented in this research. We start off by talking about the concerning increase in information on social media about mental health issues and the pressing need to create efficient monitoring mechanisms. The research explores the several methods used to identify the subtleties of suicidal thought conveyed in text, photographs, and audio-visual information. These methods include sentiment analysis, natural language processing, and deep learning models. We look at the problems with unbalanced data, privacy issues, and the moral ramifications of keeping an eye on user-generated material. We also go over the research's practical ramifications, such as the creation of instruments for real-time monitoring and crisis response techniques. Through comprehensive experiments and benchmarking, we demonstrate the potential of machine learning in providing timely support for those in need, thereby reducing the impact of suicidal ideation on society. 2023 IEEE.

Source

2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Deep learning; Detection; Early intervention; Machine learning; Mental health; Natural language processing; Online posts; Public health; Sentiment analysis; Social media; Suicidal ideation

Coverage

Nandhini B., School of Management Studies, Bannari Amman Institute of Technology, Sathyamangalam, India; Jayanthi, Hindusthan College of Engineering and Technology, Department of Management Sciences, Coimbatore, India; Shrinivas K., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Vinod P., School of Business and Management, Christ (Deemed to Be University), Bangalore, India

Rights

Restricted Access

Relation

ISBN: 979-835039324-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICCAKM58659.2023.10449591

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187553243&doi=10.1109%2fICCAKM58659.2023.10449591&partnerID=40&md5=ac44114be6969b96fb6c6c9ddb21f6b4