Cyberbullying detection on multi-modal data using pre-trained deep learning architectures
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
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J.A. Pater, A.D. Miller, E.D. Mynatt, “This digital life: a neighborhood-based study of adoles-cents’ lives online,” Proceedings of the 33rd Annual ACM Conference on Human Factors inComputing Systems, pp. 2305-2314, 2015, doi: 10.1145/2702123.2702534
D. Halpern, M. Pina and J. Vasuquez, “Loneliness,personal and social well-being:towardsa conceptualization of the effects of Cyberbullying,” Cult.y Educ., vol-29, no.4, pp.703- 727,oct-2017, doi: https://doi.org/10.1080/11356405.2017.1370818
K. Reynolds, A. Kontostathis A, L. Edwards, “Using machine learning to detect cyberbullying,” 2011 10th International conference on machine learning and applications and workshops(ICMLA), vol. 2, pp 241–244. 2011, doi: 10.1109/ICMLA.2011.152
D. Yin, Z. Xue, L. Hong, B.D. Davison, A. Kontostathis, L. Edwards, “Detection of harassment on web 2.0.,” Proc Content Anal WEB 2, 1–7, 2009.
H. Rosa, D. Matos, R. Ribeiro, L. Coheur and J. P. Carvalho, “A “Deeper” Look at DetectingCyberbullying in Social Networks,” 2018 International Joint Conference on Neural Networks(IJCNN), Rio de Janeiro, pp. 1-8, Oct-2018, doi: 10.1109/IJCNN.2018.8489211
S. Bu., S. Cho., “A Hybrid Deep Learning System of CNN and LRCN to Detect Cyberbullying from SNS Comments,” Hybrid Artificial Intelligent Systems, 2018, doi: 10.1007/978-3-319-92639-1_47
D. Van Bruwaene, Q. Huang, D. Inkpen, “A multi-platform dataset for detecting cyberbull-ying in social media,” Lang Resources & Evaluation, 54, pp.851–874, 2020, doi : https://doi.org /10.1007/s10579-020-09488-3
Zx. Liu, Dg. Zhang, Gz. Luo, et al., “A new method of emotional analysis based on CNN– Bi-LSTM hybrid neural network,” Cluster Computing, 23, pp.2901–2913, 2020. doi : https://doi.org/10.1007/s10586-020-03055-9
S.S. Shylaja, Abhishek Narayanan, Abhijith Venugopal, Abhishek Prasad, “Recurrent NeuralNetwork Architectures with Trained Document Embeddings for Flagging Cyber-AggressiveComments on Social media,” International Conference on Advanced Computing andCommunications (ADCOM), 2018.
F. Frederick, Patacsil, “Analysis of Cyberbullying Incidence among Filipina Victims: A PatternRecognition using Association Rule Extraction,” International Journal of Intelligent Systemsand Applications (IJISA), vol.11, no.11, pp.48-57, 2019, doi: 10.5815/ijisa.2019.11.05
J. de-la-Peña-Sordo, I. Pastor-López, X. Ugarte-Pedrero, I. Santos and P. G. Bringas, “Anomaly-based user comments detection in social news websites using troll user comments as nor-mality representation,” Logic Journal of the IGPL, vol. 24, no. 6, pp. 883-898, 2016, doi: 10.1093/jigpal/jzw043.
G.O. Gutiérrez-Esparza, M. Vallejo-Allende, J. Hernández-Torruco, “Classification of Cyber-Aggression Cases Applying Machine Learning,” Appl. Sci. 9, no. 9, 1828., 2019, dpoi: https://doi.org/10.3390/app9091828
Haoti Zhong, Hao Li, Anna Squicciarini, Sarah Rajtmajer, Christopher Griffin, David Miller, and Cornelia Caragea, “Content-driven detection of cyberbullying on the Instagram social ne-twork,” Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence,(IJCAI’16), AAAI Press, pp,3952–3958, 2016.
R. Zhao and K. Mao, “Cyberbullying Detection Based on Semantic-Enhanced MarginalizedDenoising Auto-Encoder,” IEEE Transactions on Affective Computing, vol. 8, no. 3, pp. 328-339, 2017, doi : 10.1109/TAFFC.2016.2531682.
A. Kumar, Nitin Sachdeva. “Cyberbullying detection on social multimedia using soft com-puting techniques: a meta-analysis,” Multimedia Tools and Applications, pp. 1-38, 2019, doi:https://doi.org/10.1007/s11042-019-7234-z
X. Zhang, X. et al., “Cyberbullying Detection with a Pronunciation Based Convolutional Neural Network,” 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 740-745, 2016, doi : 10.1109/ICMLA.2016.0132.
J.K. Tripathy, S.S. Chakkaravarthy, S.C. Satapathy, et al., “ALBERT-based fine-tuning modelfor cyberbullying analysis,” Multimedia Systems, 2020, doi: https://doi.org/10.1007/s00530-020-00690-5
S. Paul, S. Saha, “CyberBERT: BERT for cyberbullying identification,” Multimedia Systems, 2020, doi : https://doi.org/10.1007/s00530-020-00710-4
K. Kumari, J.P. Singh, Y.K. Dwivedi, et al., “Towards Cyberbullying-free social media in smartcities: a unified multi-modal approach,” Soft Computing, pp.11059-11070, 2019, doi: https://doi.org/10.1007/s00500-019-04550-x
K. Kumari, J.P. Singh, “Identification of cyberbullying on multi-modal social media postsusing genetic algorithm,” Wiley, 2020, doi : https://doi.org/10.1002/ett.3907
F. Chollet, “Xception: Deep learning with depth wise separable convolutions,” Conference Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1800-1807, 2017, doi: 10.1109/CVPR.2017.195.
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, MikeLewis, Luke Zettlemoyer and Veselin Stoyanov, “ RoBERTa: A robustly optimized BERT pre-training approach,” 2019.
J. Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, “BERT: pre-training of deep bidi-rectional transformers for language understanding,” pp. 4171-4186, 2018, doi:10.18653/v1/N19-1423
Ramprasaath R selvaraju, Michael Cogswell, Abhisek Das, Ramakrishna Vedantam, DeviParikh & Dhruv Batra, “ Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,” International Journal of computer vision, pp. 336–359, 2020, doi: https://doi.org/10.1007/s11263-019-01228-7
Satya Prakash Yadhav and Sachin Yadav, “Image fusion using hybrid methods in multimo-dality medical images,” Medical & Biological Engineering& Computing,Springer, 2020, doi :https://doi.org /10.1007/s11517-020-02136-6
