Cyberbullying detection on multi-modal data using pre-trained deep learning architectures

Subbaraju Pericherla; Ilavarasan Egambaram

doi:10.16925/2357-6014.2021.03.09

Cyberbullying detection on multi-modal data using pre-trained deep learning architectures

Research Articles

https://doi.org/10.16925/2357-6014.2021.03.09

Subbaraju Pericherla Ver Biografía

Pondicherry Engineering College

E Ilavarasan Ver Biografía

Pondicherry Engineering College

Introduction: The present article is the product of the research “Cyberbullying Detection on Multi-Modal Data Using Pre-Trained Deep Learning Architectures.”, developed at Pondicherry Engineering College in the year 2020.

Problem: Identification of cyberbullying activities on multi-modal data of social mediaObjective:To propose a model that can identify cyberbullying activity for text and image data.

Methodology: This paper has extracted the features of using two pre-trained architectures for text data and image data, in order to identify cyberbullying activities on multi-modal data, concatenated text features and image features, before supplying them as inputs to the classifier.

Results: An analysis has been performed on the proposed approach implemented on multi-modal data with Recall, and F1-Score as measures. Grad-cam visualization is presented for images to show highlighting re-gions.

Conclusion: The results indicate that the proposed approach is efficient when compared with the baseline methods.

Originality: The proposed approach is effective and conceptualized to improve cyberbullying detection on mul-ti-modal data.

Limitations: There is a need to develop a model which can identify bullying graphical images and videos

Keywords: cyberbullying, social media, natural language processing, RoBERTa, Xception, deep learning

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S. Pericherla and I. Egambaram, “Cyberbullying detection on multi-modal data using pre-trained deep learning architectures”, ing. Solidar, vol. 17, no. 3, pp. 1–20, Sep. 2021, doi: 10.16925/2357-6014.2021.03.09.

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Vol. 17 No. 3 (2021)

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2021-09-06

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