Machine Learning in Cyberbullying Detection from Social-Media Image or Screenshot with Optical Character Recognition

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Tofayet Sultan 1,* Nusrat Jahan 1 Ritu Basak 1 Mohammed Shaheen Alam Jony 1 Rashidul Hasan Nabil 1

1. Department of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh

* Corresponding author.


Received: 25 Sep. 2022 / Revised: 3 Dec. 2022 / Accepted: 4 Jan. 2023 / Published: 8 Apr. 2023

Index Terms

Cyberbullying Detection, Data Mining, Machine Learning, NLP, OCR


Along with the growth of the Internet, social media usage has drastically expanded. As people share their opinions and ideas more frequently on the Internet and through various social media platforms, there has been a notable rise in the number of consumer phrases that contain sentiment data. According to reports, cyberbullying frequently leads to severe emotional and physical suffering, especially in women and young children. In certain instances, it has even been reported that sufferers attempt suicide. The bully may occasionally attempt to destroy any proof they believe to be on their side. Even if the victim gets the evidence, it will still be a long time before they get justice at that point. This work used OCR, NLP, and machine learning to detect cyberbullying in photos in order to design and execute a practical method to recognize cyberbullying from images. Eight classifier techniques are used to compare the accuracy of these algorithms against the BoW Model and the TF-IDF, two key features. These classifiers are used to understand and recognize bullying behaviors. Based on testing the suggested method on the cyberbullying dataset, it was shown that linear SVC after OCR and logistic regression perform better and achieve the best accuracy of 96 percent. This study aid in providing a good outline that shapes the methods for detecting online bullying from a screenshot with design and implementation details.

Cite This Paper

Tofayet Sultan, Nusrat Jahan, Ritu Basak, Mohammed Shaheen Alam Jony, Rashidul Hasan Nabil, "Machine Learning in Cyberbullying Detection from Social-Media Image or Screenshot with Optical Character Recognition", International Journal of Intelligent Systems and Applications(IJISA), Vol.15, No.2, pp.1-13, 2023. DOI:10.5815/ijisa.2023.02.01


[1]A.Saravanaraj, J. I. Sheeba, S. Pradeep Devaneyan, 2016. Automatic Detection of Cyberbullying from twitter. IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN.
[2]M. A. Al-Garadi, K. D. Varathan, and S. D. Ravana, “Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network,”Comput. Human Behav., vol. 63, 2016, pp. 433–443.
[3]Raisi, E. and Huang, B., 2017, July. 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).
[4]Basarslan, M.S. and Kayaalp, F., 2020. Sentiment Analysis with Machine Learning Methods on Social Media.
[5]J. Han, M. Kamber and J. Pei, “Data Mining: Concepts and Techniques,” Elsevier, Morgan Kaufmann Series in Data Management Systems, vol. 3, 2011
[6]B. Liu, “Sentiment Analysis and Opinion Mining,” Synthesis Lectures on Human Language Technologies, Morgan & Claypool, vol. 5, no. 1, pp. 1-167, May 2012
[7]Hosseinmardi, H., Rafiq, R.I., Han, R., Lv, Q. and Mishra, S., 2016, August. Prediction of cyberbullying incidents in a media-based social network. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 186-192). IEEE.
[8]Kargutkar, S.M. and Chitre, V., 2020, March. A study of cyberbullying detection using machine learning techniques. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 734-739). IEEE.
[9]Choudhary, A., Rishi, R. and Ahlawat, S., 2013. A new approach to detect and extract characters from off-line printed images and text. Procedia Computer Science, 17, pp.434-440.
[10]Akopyan, M.S., Belyaeva, O.V., Plechov, T.P. and Turdakov, D.Y., 2019, September. Text recognition on images from social media. In 2019 Ivannikov Memorial Workshop (IVMEM) (pp. 3-6). IEEE.
[11]Kumar, A. and Sachdeva, N., 2021. Multimodal cyberbullying detection using capsule network with dynamic routing and deep convolutional neural network. Multimedia Systems, pp.1-10.
[12]Ranjan S, Sanket S, Singh S, Tyagi S, Kaur M, Rakesh N, Nand P. OCR based Automated Number Plate Text Detection and Extraction. In2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) 2022 Mar 23 (pp. 621-627). IEEE.
[13]Zhao, R., Zhou, A. and Mao, K., 2016, January. Automatic detection of cyberbullying on social networks based on bullying features. In Proceedings of the 17th international conference on distributed computing and networking (pp. 1-6).
[14]Drishya, S.V., Saranya, S., Sheeba, J.I. and Devaneyan, S.P., 2019. Cyberbully image and text detection using convolutional neural networks. CiiT International Journal of Fuzzy Systems, 11(2), pp.25-30.
[15]Islam, M.M., Uddin, M.A., Islam, L., Akter, A., Sharmin, S. and Acharjee, U.K., 2020, December. Cyberbullying detection on social networks using machine learning approaches. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-6). IEEE.
[16]Perera, A. and Fernando, P., 2021. Accurate cyberbullying detection and prevention on social media. Procedia Computer Science, 181, pp.605-611.
[17]Alam, K.S., Bhowmik, S. and Prosun, P.R.K., 2021, February. Cyberbullying detection: an ensemble based machine learning approach. In 2021 third international conference on intelligent communication technologies and virtual mobile networks (ICICV) (pp. 710-715). IEEE.
[18]Muneer, A. and Fati, S.M., 2020. A comparative analysis of machine learning techniques for cyberbullying detection on Twitter. Future Internet, 12(11), p.187.
[19]Hani, J., Mohamed, N., Ahmed, M., Emad, Z., Amer, E. and Ammar, M., 2019. Social media cyberbullying detection using machine learning. International Journal of Advanced Computer Science and Applications, 10(5).
[20]Wan Noor Hamiza Wan Ali, Masnizah Mohd, Fariza Fauzi, Centre for Cyber Security, Universiti Kebangsaan Malaysia Bangi, Selangor, 2020. Cyberbullying Predictive Model: Implementation of Machine Learning Approach.
[21]Kumar, A., Nayak, S. and Chandra, N., 2019. Empirical analysis of supervised machine learning techniques for Cyberbullying detection. In International Conference on Innovative Computing and Communications (pp. 223-230). Springer, Singapore.
[22]Hani, J., Nashaat, M., Ahmed, M., Emad, Z., Amer, E. and Mohammed, A., 2019. Social media cyberbullying detection using machine learning. Int. J. Adv. Comput. Sci. Appl, 10(5), pp.703-707.
[23]Monirah Abdullah Al-Ajlan, Mourad Ykhle, King Saud University, 2018. Deep Learning Algorithm for Cyberbullying Detection. (IJACSA) International Journal of Advanced Computer Science and Applications.
[24]Elsafoury, Fatma (2020), “Cyberbullying datasets”, Mendeley Data, V1, doi: 10.17632/jf4pzyvnpj.1