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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Veröffentlicht in:	IEEE transactions on affective computing Jg. 8; H. 3; S. 328 - 339
Hauptverfasser:	Zhao, Rui, Mao, Kezhi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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