Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal post...

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Bibliographic Details
Published in:Algorithms Vol. 13; no. 1; p. 7
Main Authors: Tadesse, Michael Mesfin, Lin, Hongfei, Xu, Bo, Yang, Liang
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-01-2020
Subjects:
Celebrities
Classification
Computer architecture
Deep learning
Diagnosis
Digital media
early suicide detection
Health aspects
Information dissemination
linguistic metadata
Machine learning
Neural networks
Psychological aspects
reddit social media
Risk assessment
Social media
Social networks
Suicidal ideation
suicide ideation
Suicides & suicide attempts
word embedding
China
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Summary:Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.
ISSN:1999-4893
1999-4893
DOI:10.3390/a13010007