International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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An Ensemble Approach for Cyber Bullying Text messages and Images

  • Zarapala Sunitha Bai (Department of Computer Science and Engineering, R.V.R. and J.C. College of Engineering, Acharya Nagarjuna University) ;
  • Sreelatha Malempati (Department of Computer Science and Engineering, R.V.R. and J.C. College of Engineering)
  • Received : 2023.11.05
  • Published : 2023.11.30
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Abstract

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.

Keywords

Acknowledgement

This work is done under the grant received (6/41) by Deanship of research at Islamic University of Madinah (IUM) for research that studies the economic effect of COvid-19 pandemic. We also give special thanks to the administration of IUM for their support in every aspect.

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