Citation

Al Hashedi, Mohammed and Soon, Lay Ki and Goh, Hui Ngo (2019) Cyberbullying Detection Using Deep Learning and Word Embeddings: An Empirical Study. In: 2019 2nd International Conference on Computational Intelligence and Intelligent Systems, 23-25 Nov. 2019, Bangkok, Thailand.

Text 129.pdf - Published Version Restricted to Repository staff only Download (449kB)

Abstract

Cyberbullying detection has become a pressing need in Internet usage governance due to its harmful consequences. Different approaches have been proposed to tackle this problem, including deep learning. In this paper, an empirical study is conducted to evaluate the effectiveness and efficiency of deep learning algorithms, coupled with word embeddings in detecting cyberbullying texts. Three deep learning algorithms were experimented, namely GRU, LSTM and BLSTM. Data pre-processing steps, including oversampling were performed on the selected social media datasets. For feature representations, four different word embeddings models were explored, including word2vec, GloVe, Reddit and ELMO models. Elmo cares of word context by capturing information from the word surroundings which eliminates some of the shortcomings of pre-trained word embeddings models. For more accurate results, 10-fold cross-validation technique was implemented. The experimental results show that BLSTM performs best with ELMO in detecting cyberbullying texts. The efficiency of each model is also measured by calculating the average time taken for training each model. GRU outperforms in terms of time efficiency. Based on the analysis done on false negative cases, three observations were made, which highlight the limitations of word embeddings models on top of GRU algorithm in cyberbullying detection.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Cyberbullying, Word Embeddings, Deep Learning, ELMo
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare. Criminology > HV6001-7220.5 Criminology > HV6251-6773.55 Crimes and offenses
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	15 Oct 2021 06:36
Last Modified:	15 Oct 2021 06:36
URII:	http://shdl.mmu.edu.my/id/eprint/9558

Downloads

Edit (login required)