TY - GEN
T1 - Identification and Classification of Cyberbullying Posts
T2 - 27th International Conference on Neural Information Processing, ICONIP 2020
AU - Agarwal, Ayush
AU - Chivukula, Aneesh Sreevallabh
AU - Bhuyan, Monowar H.
AU - Jan, Tony
AU - Narayan, Bhuva
AU - Prasad, Mukesh
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - With the number of users of social media and web platforms increasing day-by-day in recent years, cyberbullying has become a ubiquitous problem on the internet. Controlling and moderating these social media platforms manually for online abuse and cyberbullying has become a very challenging task. This paper proposes a Recurrent Neural Network (RNN) based approach for the identification and classification of cyberbullying posts. In highly imbalanced input data, a Tomek Links approach does under-sampling to reduce the data imbalance and remove ambiguities in class labelling. Further, the proposed classification model uses Max-Pooling in combination with Bi-directional Long Short-Term Memory (LSTM) network and attention layers. The proposed model is evaluated using Wikipedia datasets to establish the effectiveness of identifying and classifying cyberbullying posts. The extensive experimental results show that our approach performs well in comparison to competing approaches in terms of precision, recall, with F1 score as 0.89, 0.86 and 0.88, respectively.
AB - With the number of users of social media and web platforms increasing day-by-day in recent years, cyberbullying has become a ubiquitous problem on the internet. Controlling and moderating these social media platforms manually for online abuse and cyberbullying has become a very challenging task. This paper proposes a Recurrent Neural Network (RNN) based approach for the identification and classification of cyberbullying posts. In highly imbalanced input data, a Tomek Links approach does under-sampling to reduce the data imbalance and remove ambiguities in class labelling. Further, the proposed classification model uses Max-Pooling in combination with Bi-directional Long Short-Term Memory (LSTM) network and attention layers. The proposed model is evaluated using Wikipedia datasets to establish the effectiveness of identifying and classifying cyberbullying posts. The extensive experimental results show that our approach performs well in comparison to competing approaches in terms of precision, recall, with F1 score as 0.89, 0.86 and 0.88, respectively.
KW - Cyberbullying
KW - Natural language processing
KW - Recurrent Neural Network
KW - Social media
KW - Under-sampling
UR - http://www.scopus.com/inward/record.url?scp=85097055904&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-63823-8_14
DO - 10.1007/978-3-030-63823-8_14
M3 - Conference contribution
AN - SCOPUS:85097055904
SN - 9783030638221
T3 - Communications in Computer and Information Science
SP - 113
EP - 120
BT - Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
A2 - Yang, Haiqin
A2 - Pasupa, Kitsuchart
A2 - Leung, Andrew Chi-Sing
A2 - Kwok, James T.
A2 - Chan, Jonathan H.
A2 - King, Irwin
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 November 2020 through 22 November 2020
ER -
