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Multimedia Systems
Published in: Multimedia Systems 6/2022

02-02-2021 | Special Issue Paper

Multimodal cyberbullying detection using capsule network with dynamic routing and deep convolutional neural network

Authors: Akshi Kumar, Nitin Sachdeva

Published in: Multimedia Systems | Issue 6/2022

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Abstract

Cyberbullying is the use of information technology networks by individuals’ to humiliate, tease, embarrass, taunt, defame and disparage a target without any face-to-face contact. Social media is the 'virtual playground' used by bullies with the upsurge of social networking sites such as Facebook, Instagram, YouTube and Twitter. It is critical to implement models and systems for automatic detection and resolution of bullying content available online as the ramifications can lead to a societal epidemic. This paper presents a deep neural model for cyberbullying detection in three different modalities of social data, namely textual, visual and info-graphic (text embedded along with an image). The all-in-one architecture, CapsNet–ConvNet, consists of a capsule network (CapsNet) deep neural network with dynamic routing for predicting the textual bullying content and a convolution neural network (ConvNet) for predicting the visual bullying content. The info-graphic content is discretized by separating text from the image using Google Lens of Google Photos app. The perceptron-based decision-level late fusion strategy for multimodal learning is used to dynamically combine the predictions of discrete modalities and output the final category as bullying or non-bullying type. Experimental evaluation is done on a mix-modal dataset which contains 10,000 comments and posts scrapped from YouTube, Instagram and Twitter. The proposed model achieves a superlative performance with the AUC–ROC of 0.98.
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Footnotes
Literature
1.
Kumar, A., Sharma, H.: PROD: A potential rumour origin detection model using supervised machine learning. In: International Conference on Intelligent Computing and Smart Communication 2019, Springer, pp. 1269–1276 (2020)
2.
Campbell, M.A.: Cyber bullying: an old problem in a new guise? J. Psychol. Couns. Sch. 15(1), 68–76 (2005)
3.
Kumar, A., Sachdeva, N.: Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis. Multimed. Tools Appl. 78(17), 23973–24010 (2019)CrossRef
4.
5.
6.
Kumar, A., Srinivasan, K., Cheng, W.H., Zomaya, A.Y.: Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Inf. Process. Manag. 57(1), 102141 (2020)CrossRef
7.
8.
Kumar, A., Jaiswal, A.: A deep swarm-optimized model for leveraging industrial data analytics in cognitive manufacturing. IEEE Trans. Ind. Inform. (2020)
9.
Nimmi, K., Menon, V.G., Janet, B., Kumar, A.: Deep Learning for Next-Generation Inventive Wireless Networks: Issues, Challenges, and Future Directions. Handbook of Research on Emerging Trends and Applications of Machine Learning, pp. 183–199. IGI Global, Philadelphia (2020)
10.
Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55–75 (2017)CrossRef
11.
Dadvar, M., & Eckert, K.: Cyberbullying detection in social networks using deep learning based models; a reproducibility study. arXiv preprint arXiv:​1812.​08046 (2018)
12.
Zhao, W., Ye, J., Yang, M., Lei, Z., Zhang, S., Zhao, Z.: Investigating capsule networks with dynamic routing for text classification. arXiv preprint arXiv:​1804.​00538 (2018)
13.
Kim, J., Jang, S., Park, E., Choi, S.: Text classification using capsules. Neurocomputing 376, 214–221 (2019)CrossRef
14.
Rawat, W., Wang, Z.: Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 29(9), 2352–2449 (2017)MathSciNetCrossRefMATH
15.
Tripathi, A.K., Sharma, K., Bala, M., Kumar, A., Menon, V.G., Bashir, A.K.: A parallel military dog based algorithm for clustering big data in cognitive industrial internet of things. IEEE Trans. Ind. Inform. (2020)
16.
Dinakar, K., Jones, B., Havasi, C., Lieberman, H., Picard, R.: Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Trans. Interact. Intell. Syst. (TiiS) 2(3), 18 (2012)
17.
Hinduja, S., Patchin, J.W.: Bullying, cyberbullying, and suicide. Arch. Suicide Res. 14(3), 206–221 (2010)CrossRef
18.
Kokkinos, C.M., Antoniadou, N., Markos, A.: Cyber-bullying: an investigation of the psychological profile of university student participants. J. Appl. Dev. Psychol. 35(3), 204–214 (2014)CrossRef
19.
Dadvar, M., Jong, F.D., Ordelman, R., Trieschnigg, D.: Improved cyberbullying detection using gender information. In: Proceedings of the Twelfth Dutch-Belgian Information Retrieval Workshop (DIR 2012). University of Ghent, (2012)
20.
Dadvar, M., Trieschnigg, D., Ordelman, R., de Jong, F.: Improving cyberbullying detection with user context. In: European Conference on Information Retrieval, pp. 693–696. Springer, Berlin (2013)
21.
Nahar, V., Unankard, S., Li, X., Pang, C.: Sentiment analysis for effective detection of cyber bullying. In: Asia-Pacific Web Conference, pp. 767–774. Springer, Berlin (2012)
22.
Nahar, V., Al-Maskari, S., Li, X., Pang, C.: Semi-supervised learning for cyberbullying detection in social networks. In: Australasian Database Conference, pp. 160–171. Springer, Cham (2014)
23.
Reynolds, K., Kontostathis, A., Edwards, L.: Using machine learning to detect cyberbullying. In: 2011 10th International Conference on Machine learning and applications and workshops, vol. 2, pp. 241–244. IEEE. (2011)
24.
Michal, P., Pawel, D., Tatsuaki, M., Fumito, M., Rafal, R., Kenji, A., Yoshio, M.: In the service of online order: tackling cyber-bullying with machine learning and affect analysis. Int. J. Comput. Linguist. Res. 1(3), 135–154 (2010)
25.
Yin, D., Xue, Z., Hong, L., Davison, B.D., Kontostathis, A., Edwards, L.: Detection of harassment on web 2.0. Proc. Content Anal. WEB 2, 1–7 (2009)
26.
Van Hee, C., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., De Pauw, G., Daelemans, W., Hoste, V.: Automatic detection and prevention of cyberbullying. In: International Conference on Human and Social Analytics (HUSO 2015), pp. 13–18. IARIA (2015)
27.
Van Hee, C., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., De Pauw, G., Daelemans, W., Hoste, V.: Detection and fine-grained classification of cyberbullying events. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, pp. 672–680. (2015)
28.
Al-garadi, M.A., Varathan, K.D., Ravana, S.D.: Cybercrime detection in online communications: the experimental case of cyberbullying detection in the Twitter network. Comput. Hum. Behav. 63, 433–443 (2016)CrossRef
29.
Xu, J.M., Jun, K.S., Zhu, X., Bellmore, A.: Learning from bullying traces in social media. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 656–666. Association for Computational Linguistics (2012)
30.
Zhao, R., Zhou, A., Mao, K.: Automatic detection of cyberbullying on social networks based on bullying features. In: Proceedings of the 17th International Conference on Distributed Computing and Networking, p. 43. ACM. (2016)
31.
Raisi, E., Huang, B.: Cyberbullying detection with weakly supervised machine learning. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 409–416. ACM. (2017)
32.
Agrawal, S., Awekar, A.: Deep learning for detecting cyberbullying across multiple social media platforms. In: European Conference on Information Retrieval, pp. 141–153. Springer, Cham (2018)
33.
Huang, Q., Singh, V.K., Atrey, P.K.: Cyber bullying detection using social and textual analysis. In: Proceedings of the 3rd International Workshop on Socially-Aware Multimedia, pp. 3–6. (2014)
34.
Xu, Z., Zhu, S.: Filtering offensive language in online communities using grammatical relations. In: Proceedings of the Seventh Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, pp. 1–10. (2010)
35.
Cheng, L., Guo, R., Silva, Y., Hall, D., Liu, H.: Hierarchical attention networks for cyberbullying detection on the Instagram social network. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 235–243. Society for Industrial and Applied Mathematics. (2019)
36.
Al-Hashedi, M., Soon, L.K., Goh, H.N.: Cyberbullying detection using deep learning and word embeddings: an empirical study. In: Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems, pp. 17–21. (2019)
37.
Founta, A.M., Chatzakou, D., Kourtellis, N., Blackburn, J., Vakali, A., Leontiadis, I.: A unified deep learning architecture for abuse detection. In: Proceedings of the 10th ACM Conference on Web Science, pp. 105–114. (2019)
38.
Mahlangu, T., Tu, C.: Deep learning cyberbullying detection using stacked embbedings approach. In: 2019 6th International Conference on Soft Computing and Machine Intelligence (ISCMI), pp. 45–49. IEEE. (2019)
39.
Kansara, K.B., Shekokar, N.M.: A framework for cyberbullying detection in social network. Int. J. Curr. Eng. Technol. 5(1), 494–498 (2015)
40.
Singh, V.K., Ghosh, S., Jose, C.: Toward multimodal cyberbullying detection. In: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 2090–2099. (2017)
41.
Yang, F., Peng, X., Ghosh, G., Shilon, R., Ma, H., Moore, E., Predovic, G.: Exploring deep multimodal fusion of text and photo for hate speech classification. In: Proceedings of the Third Workshop on Abusive Language Online, pp. 11–18. (2019)
42.
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in neural information processing systems, pp. 3856–3866. (2017)
43.
Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., Zettlemoyer, L.: Deep contextualized word representations. arXiv preprint arXiv:​1802.​05365. (2018)
44.
Caldeira, M., Martins, P., Costa, R.L.C., Furtado, P.: Image Classification Benchmark (ICB). Expert Syst. Appl. 142, 112998 (2020)CrossRef
45.
Srivastava, S., Khurana, P., Tewari, V.: Identifying aggression and toxicity in comments using capsule network. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pp. 98–105. (2018)
46.
Alkhawlani, M., Elmogy, M., Elbakry, H.: Content-based image retrieval using local features descriptors and bag-of-visual words. Int. J. Adv. Comput. Sci. Appl. 6(9), 212–219 (2015)
Metadata
Title
Multimodal cyberbullying detection using capsule network with dynamic routing and deep convolutional neural network
Authors
Akshi Kumar
Nitin Sachdeva
Publication date
02-02-2021
Publisher
Springer Berlin Heidelberg
Published in
Multimedia Systems / Issue 6/2022
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-020-00747-5

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