Abstract
Cyberbullying (CB) has become increasingly prevalent in social media platforms. With the popularity and widespread use of social media by individuals of all ages, it is vital to make social media platforms safer from cyberbullying. This paper presents a hybrid deep learning model, called DEA-RNN, to detect CB on Twitter social media network. The proposed DEA-RNN model combines Elman type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) for fine-tuning the Elman RNN’s parameters and reducing training time. We evaluated DEA-RNN thoroughly utilizing a dataset of 10000 tweets and compared its performance to those of state-of-the-art algorithms such as Bi-directional long short term memory (Bi-LSTM), RNN, SVM, Multinomial Naive Bayes (MNB), Random Forests (RF). The experimental results show that DEA-RNN was found to be superior in all the scenarios. It outperformed the considered existing approaches in detecting CB on Twitter platform. DEA-RNN was more efficient in scenario 3, where it has achieved an average of 90.45% accuracy, 89.52% precision, 88.98% recall, 89.25% F1-score, and 90.94% specificity.
Highlights
Social media networks such as Facebook, Twitter, Flickr, and Instagram have become the preferred online platforms for interaction and socialization among people of all ages
Two baseline cyberbullying models based on deep learning, namely Bi-directional long short term memory (Bi-LSTM) [21], Recurrent Neural Networks (RNN)[21], and three baseline cyberbullying models based on machine learning models, namely, SVM [26], Multinomial Naive Bayes (MNB) [11], and Random Forests (RF)[11] are used for the comparison with the proposed Dolphin Echolocation Algorithm (DEA)-RNN model
This paper developed an efficient tweet classification model to enhance the effectiveness of topic models for the detection of cyber-bullying events
Summary
Social media networks such as Facebook, Twitter, Flickr, and Instagram have become the preferred online platforms for interaction and socialization among people of all ages. While these platforms enable people to communicate and interact in previously unthinkable ways, they have led to malevolent activities such as cyber-bullying. Most suicides are due to the anxiety, depression, stress, and social and emotional difficulties from cyber-bullying events [2],[3], and [4]. This motivates the need for an approach to identify cyberbullying in social media messages (e.g., posts, tweets, and comments)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
