Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique

被引:2
|
作者
El Koshiry, Amr Mohamed [1 ,2 ]
Eliwa, Entesar Hamed I. [3 ,4 ]
Abd El-Hafeez, Tarek [4 ,5 ]
Khairy, Marwa [6 ]
机构
[1] King Faisal Univ, Coll Educ, Dept Curricula & Teaching Methods, Al Hasa, Saudi Arabia
[2] Minia Univ, Fac Specif Educ, Al Minya, Egypt
[3] King Faisal Univ, Coll Sci, Dept Math & Stat, Al Hasa, Saudi Arabia
[4] Minia Univ, Fac Sci, Dept Comp Sci, Al Minya, Egypt
[5] Deraya Univ, Comp Sci Unit, Al Minya, Egypt
[6] Minia Univ, Fac Comp & Informat, Dept Comp Sci, EL Minia, Egypt
关键词
Cyberbullying; Machine learning; Deep learning; LSTM; GRU; NETWORKS;
D O I
10.7717/peerj-cs.1961
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This study investigates the effectiveness of various deep learning and classical machine learning techniques in identifying instances of cyberbullying. The study compares the performance of five classical machine learning algorithms and three deep learning models. The data undergoes pre-processing, including text cleaning, tokenization, stemming, and stop word removal. The experiment uses accuracy, precision, recall, and F1 score metrics to evaluate the performance of the algorithms on the dataset. The results show that the proposed technique achieves high accuracy, precision, and F1 score values, with the Focal Loss algorithm achieving the highest accuracy of 99% and the highest precision of 86.72%. However, the recall values were relatively low for most algorithms, indicating that they struggled to identify all relevant data. Additionally, the study proposes a technique using a convolutional neural network with a bidirectional long short-term memory layer, trained on a pre-processed dataset of tweets using GloVe word embeddings and the focal loss function. The model achieved high accuracy, precision, and F1 score values, with the GRU algorithm achieving the highest accuracy of 97.0% and the NB algorithm achieving the highest precision of 96.6%.
引用
收藏
页数:33
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