Cyberbullying Detection Based on Semantic-Enhanced Marginalized Denoising Auto-Encoder

As a side effect of increasingly popular social media, cyberbullying has emerged as a serious problem afflicting children, adolescents and young adults. Machine learning techniques make automatic detection of bullying messages in social media possible, and this could help to construct a healthy and...

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Bibliographic Details
Published in:IEEE transactions on affective computing Vol. 8; no. 3; pp. 328 - 339
Main Authors: Zhao, Rui, Mao, Kezhi
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adolescents
Adults
Analytical models
Bullying
Children
Cyberbullying
Cyberbullying detection
Digital media
Feature extraction
Machine learning
Mathematical models
Media
Messages
Noise reduction
Numerical models
representation learning
Representations
Robustness
Robustness (mathematics)
Semantics
Short message service
Social networks
stacked denoising autoencoders
Teaching methods
text mining
word embedding
ISSN:1949-3045, 1949-3045
Online Access:Get full text
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Summary:As a side effect of increasingly popular social media, cyberbullying has emerged as a serious problem afflicting children, adolescents and young adults. Machine learning techniques make automatic detection of bullying messages in social media possible, and this could help to construct a healthy and safe social media environment. In this meaningful research area, one critical issue is robust and discriminative numerical representation learning of text messages. In this paper, we propose a new representation learning method to tackle this problem. Our method named semantic-enhanced marginalized denoising auto-encoder (smSDA) is developed via semantic extension of the popular deep learning model stacked denoising autoencoder (SDA). The semantic extension consists of semantic dropout noise and sparsity constraints, where the semantic dropout noise is designed based on domain knowledge and the word embedding technique. Our proposed method is able to exploit the hidden feature structure of bullying information and learn a robust and discriminative representation of text. Comprehensive experiments on two public cyberbullying corpora (Twitter and MySpace) are conducted, and the results show that our proposed approaches outperform other baseline text representation learning methods.
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ISSN:1949-3045
1949-3045
DOI:10.1109/TAFFC.2016.2531682