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Bilingual Cyber-aggression Detection on Social Media using LSTM Autoencoder
Kumari, K. Singh, J.P. Dwivedi, Y.K. Rana, Nripendra P.
Kumari, K.
Singh, J.P.
Dwivedi, Y.K.
Rana, Nripendra P.
Publication Date
2021-07
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(c) 2021 SpringerNature. Full-text reproduced in accordance with the publisher's self-archiving policy.
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openAccess
Accepted for publication
05/04/2021
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Abstract
Cyber-aggression is an offensive behaviour attacking people based on
race, ethnicity, religion, gender, sexual orientation, and other traits. It has become
a major issue plaguing the online social media. In this research, we have developed
a deep learning-based model to identify different levels of aggression (direct, indirect and no aggression) in a social media post in a bilingual scenario. The model
is an autoencoder built using the LSTM network and trained with non-aggressive
comments only. Any aggressive comment (direct or indirect) will be regarded as
an anomaly to the system and will be marked as Overtly (direct) or Covertly
(indirect) aggressive comment depending on the reconstruction loss by the autoencoder. The validation results on the dataset from two popular social media
sites: Facebook and Twitter with bilingual (English and Hindi) data outperformed
the current state-of-the-art models with improvements of more than 11% on the
test sets of the English dataset and more than 6% on the test sets of the Hindi
dataset.
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Accepted manuscript
Citation
Kumari K, Singh JP, Dwivedi YK et al (2021) Bilingual Cyber-aggression Detection on Social Media using LSTM Autoencoder. Soft Computing. 25: 8999-9012.
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Article