Measuring the Extent of Cyberbullying Comments in Facebook Groups for Mosul University Students
Article Sidebar
Main Article Content
Abstract
The widespread utilization of social media platforms such as Facebook, Twitter, and Instagram inside academic organizations has become fundamental for student correspondence and joint effort, yet it has simultaneously prompted an increase in cyberbullying incidents. Cyberbullying is embodied by offensive and harmful comments that sabotage the casualty's prosperity. This study plans to resolve this issue by fostering an extensive module to distinguish cases of cyberbullying inside Facebook groups of Mosul University students. Our methodology starts with the collection of data in the Arabic language, which is then exposed to careful manual handling to label comments and eliminate clamor-like copies and superfluous sections. The dataset, comprising 2,715 comments, goes through prehandling and element extraction via the term frequency-inverse document frequency (TF-IDF) strategy. Consequently, we utilize the logistic regression (LR) classifier to investigate and order the data. Our findings revealed that more than 21% of the comments dissected were brutal, a disturbing rate for a local area such as Mosul University. These outcomes highlight the critical requirements for intercession and the significance of observing student cooperation on social media. The dataset and experiences obtained from this study are important for psychologists and psychological direction experts to comprehend and relieve cyberbullying. This exploration not only highlights the prevalence of cyberbullying in academic settings but also provides a strong systemic structure for future examinations to expand, highlighting the basic role of AI in battling web harassment.
Article Details
Issue
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
[1] N. I. ALIYU, A. Musbau Dogo, F. O. Ajibade, and T. Abdurauf, “Analysis of Cyber Bullying on Facebook Using Text Mining,” Journal of Applied Artificial Intelligence, vol. 1, no. 1, pp. 1–12, Dec. 2020, doi: https://doi.org/10.48185/jaai.v1i1.30.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
[2] K. Sundus, F. Al-Haj, and B. Hammo, “A Deep Learning Approach for Arabic Text Classification,” IEEE Xplore, Oct. 01, 2019. doi:https://ieeexplore.ieee.org/abstract/document/8923083 (accessed Nov. 10, 2022).
[3] A. Elnagar, R. Al-Debsi, and O. Einea, “Arabic text classification using deep learning models,” Information Processing & Management, vol. 57, no. 1, p. 102121, Jan. 2020, doi: doi:https://doi.org/10.1016/j.ipm.2019.102121.
[4] Galal, M., Madbouly, M. M., & El-Zoghby, A. D. E. L. “Classifying Arabic text using deep learning”, Journal of Theoretical and Applied Information Technology, 97(23), 3412-3422. ,2019, https://www.jatit.org/volumes/Vol97No23/1Vol97No23.pdf.
[5] M. Alhawarat and A. O. Aseeri, “A Superior Arabic Text Categorization Deep Model (SATCDM),” IEEE Access, vol. 8, pp. 24653–24661, 2020, doi: https://doi.org/10.1109/access.2020.2970504.
[6] Gwad, W. H. G., Ismael, I. M. I., & Gültepe, Y. ,“Twitter Sentiment Analysis Classification in the Arabic Language using Long Short-Term Memory Neural Networks,” International Journal of Engineering and Advanced Technology, vol. 9, no. 3, pp. 235–239, Feb. 2020, doi: https://doi.org/10.35940/ijeat.b4565.029320.
[7] F. El-Alami, A. El Mahdaouy, S. O. El Alaoui, and N. En-Nahnahi, “A DEEP AUTOENCODER-BASED REPRESENTATION FOR ARABIC TEXT CATEGORIZATION,” Journal of Information and Communication Technology, vol. 19, no. 3, pp. 381–398, Jun. 2020, doi: https://doi.org/10.32890/jict2020.19.3.4.
[8] V. Balakrishnan, S. Khan, and H. R. Arabnia, “Improving cyberbullying detection using Twitter users’ psychological features and machine learning,” Computers & Security, vol. 90, p. 101710, Mar. 2020, doi: https://doi.org/10.1016/j.cose.2019.101710.
[9] T. Sabri, O. E. Beggar, and M. Kissi, “Comparative study of Arabic text classification using feature vectorization methods,” Procedia Computer Science, vol. 198, pp. 269–275, 2022, doi: https://doi.org/10.1016/j.procs.2021.12.239.
[10] Noipom, R., Lim, A., & Sittichai, R. “Determinants of cyberbullying perpetration on Facebook among secondary school students in Southern Thailand”, Social Psychology of Education, 1-17.,2023, https://doi.org/10.1007/s11218-023-09766-2.
[11] S. M. Fati, A. Muneer, A. Alwadain, and A. O. Balogun, “Cyberbullying Detection on Twitter Using Deep Learning-Based Attention Mechanisms and Continuous Bag of Words Feature Extraction,” Mathematics, vol. 11, no. 16, p. 3567, Jan. 2023, doi: https://doi.org/10.3390/math11163567.
[12] A. Bozyiğit, S. Utku, and E. Nasibov, “Cyberbullying Detection: Utilizing Social Media Features,” Expert Systems with Applications, vol. 179, p. 115001, Apr. 2021, doi: https://doi.org/10.1016/j.eswa.2021.115001.
[13] S. Agrawal and Amit Awekar, “Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms,” arXiv (Cornell University), Jan. 2018, https://doi.org/10.1007/978-3-319-76941-7_11.
[14] S. Agrawal and Amit Awekar, “Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms,” arXiv (Cornell University), Jan. 2018, https://doi.org/10.1007/978-3-319-76941-7_11.
[15] A. Muneer, S. M. Taib, S. Naseer, R. F. Ali, and I. A. Aziz, “Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis,” Electronics, vol. 10, no. 20, p. 2453, Oct. 2021, doi: https://doi.org/10.3390/electronics10202453.
[16] S. Naseer, S. M. Fati, A. Muneer, and R. F. Ali, “iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations,” IEEE Access, vol. 10, pp. 12953–12965, 2022, doi: https://doi.org/10.1109/access.2022.3144226.
[17] H. Ali, H. Farman, H. Yar, Z. Khan, S. Habib, and A. Ammar, “Deep learning-based election results prediction using Twitter activity,” Soft Computing, Dec. 2021, doi: https://doi.org/10.1007/s00500-021-06569-5.
[18] H. El Rifai, L. Al Qadi, and A. Elnagar, “Arabic text classification: the need for multilabelling systems,” Neural Computing and Applications, vol. 34, no. 2, pp. 1135–1159, Sep. 2021, doi: https://doi.org/10.1007/s00521-021-06390-z.
[19] D. Badawi, “Intelligent Recommendations Based on COVID-19 Related Twitter Sentiment Analysis and Fake Tweet Detection in Apache Spark Environment,” IETE Journal of Research, pp. 1–24, Jul. 2023, doi: https://doi.org/10.1080/03772063.2023.2220710.
[20] K. S. Alam, S. Bhowmik, and P. R. K. Prosun, “Cyberbullying Detection: An Ensemble Based Machine Learning Approach,” 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Feb. 2021, doi: https://doi.org/10.1109/icicv50876.2021.9388499.
[21] E. Elgeldawi, A. Sayed, A. R. Galal, and A. M. Zaki, “Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis,” Informatics, vol. 8, no. 4, p. 79, Nov. 2021, doi: https://doi.org/10.3390/informatics8040079.
[22] A. Y. Muaad et al., “Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques,” Mathematical Problems in Engineering, vol. 2022, pp. 1–16, Apr. 2022, doi: https://doi.org/10.1155/2022/3720358.
[23] M. Fortunatus, P. Anthony, and S. Charters, “Combining textual features to detect cyberbullying in social media posts,” Procedia Computer Science, vol. 176, pp. 612–621, 2020, doi: https://doi.org/10.1016/j.procs.2020.08.063.
[24] A. Setyanto et al., “Arabic Language Opinion Mining Based on Long Short-Term Memory (LSTM),” Applied Sciences, vol. 12, no. 9, p. 4140, Apr. 2022, doi: https://doi.org/10.3390/app12094140.
[25] S. K. Sajithra K, “Social Media – History and Components,” IOSR Journal of Business and Management, vol. 7, no. 1, pp. 69–74, 2013, doi: https://doi.org/10.9790/487x-0716974.
[26] S. A. Salloum, M. Al-Emran, A. A. Monem, and K. Shaalan, “A Survey of Text Mining in Social Media: Facebook and Twitter Perspectives,” Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 1, pp. 127–133, Jan. 2017, doi: https://doi.org/10.25046/aj020115.
[27] R. Soliman and S. Khalil, “The teaching of Arabic as a community language in the UK,” International Journal of Bilingual Education and Bilingualism, pp. 1–12, May 2022, doi: https://doi.org/10.1080/13670050.2022.2063686.
[28] A. L. Samuel, “Some studies in machine learning using the game of checkers,” IBM Journal of Research and Development, vol. 44, no. 1.2, pp. 206–226, Jan. 2000, doi: https://doi.org/10.1147/rd.441.0206.
[29] C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,” Frontiers in Energy Research, vol. 9, Mar. 2021, doi: https://doi.org/10.3389/fenrg.2021.652801.
[30] T. Kanan, A. Aldaaja, and Bilal Hawashin, “Cyber-Bullying and Cyber-Harassment Detection Using Supervised Machine Learning Techniques in Arabic Social Media Contents,” Journal of internet Technology, vol. 21, no. 5, pp. 1409–1421, Sep. 2020, DOI: 10.3966/160792642020092105016.
[31] A El Kah, I Zeroual, “The effects of Pre-Processing Techniques on Arabic Text Classification,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 10, no. 1, pp. 41–48, Feb. 2021, doi: https://doi.org/10.30534/ijatcse/2021/061012021.
[32] Z. Jianqiang and G. Xiaolin, “Comparison Research on Text Preprocessing Methods on Twitter Sentiment Analysis,” IEEE Access, vol. 5, pp. 2870–2879, 2017, doi: https://doi.org/10.1109/access.2017.2672677.
[33] A. Y. Muaad et al., “Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques,” Mathematical Problems in Engineering, vol. 2022, pp. 1–16, Apr. 2022, doi: https://doi.org/10.1155/2022/3720358.
[34] https://github.com/saobou/arabic-text-preprocessing/blob/master/emojis.csv.
[35] Habow H, Ibrahim R, " Investigating the Dynamics of Women's Health Information on Social Media: Nursing Perspective", Malaysian Journal of Nursing (2024) 16(02) 191-200.
[36] Alhelal D, İnan T, " Designing a System to Recognize Main Arabic Dialects", IEEE Access (2024) 12 166225-166237.
[37] Seyala B, Alanezi M, " Community Detection in Facebook Using Visual Approach and Clustering", Journal of Physics: Conference Series (2021) 1804(1) 012047.
[38] L. Hussain, “Fortifying AI Against Cyber Threats Advancing Resilient Systems to Combat Adversarial Attacks”, EDRAAK, vol. 2024, pp. 26–31, Mar. 2024, doi: 10.70470/EDRAAK/2024/004.
[39] P. Dutta, A. Kumar, Şehnaz Sakici, and B. Mensah, “Enhancing Point-of-Interest Recommendation Systems through Multi-Modal Data Integration in Location-Based Social Networks: Challenges and Future Directions”, EDRAAK, vol. 2025, pp. 12–18, Jan. 2025, doi: 10.70470/EDRAAK/2025/003.