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المؤلفKumari, Kirti
المؤلفSingh, Jyoti Prakash
المؤلفDwivedi, Yogesh Kumar
المؤلفRana, Nripendra Pratap
تاريخ الإتاحة2023-06-08T10:03:03Z
تاريخ النشر2021-04-24
اسم المنشورSoft Computing
المعرّفhttp://dx.doi.org/10.1007/s00500-021-05817-y
الاقتباسKumari, K., Singh, J. P., Dwivedi, Y. K., & Rana, N. P. (2021). Bilingual Cyber-aggression detection on social media using LSTM autoencoder. Soft Computing, 25, 8999-9012.
الرقم المعياري الدولي للكتاب1432-7643
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85105131419&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/44208
الملخصCyber-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.
اللغةen
الناشرSpringer Nature
الموضوعAutoencoder
Cyber-aggression
Cyberbullying
Long Short-Term Memory
Online social networks
العنوانBilingual Cyber-aggression detection on social media using LSTM autoencoder
النوعArticle
الصفحات8999-9012
رقم العدد14
رقم المجلد25
ESSN1433-7479
dc.accessType Abstract Only


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