Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM. 1936-1955/2023/1-ART1 $15.00 http://dx.doi.org/10.1145/3604617 J. Data and Information Quality Soft Computing Techniques for Detecting Cyberbullying in Social Multimedia Data YANG JING Faculty of Computer Science and Information Technology, University of Malaya, Malaysia, Yj741655109@163.com MA HAOWEI Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Malaysia, mhwfangzhang@gmail.com ARSHIYA S. ANSARI Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia, ar.ansari@mu.edu.sa G. SUCHARITHA Department of Electronics and Communication Engineering, Institute of Aeronautical Engineering, Hyderabad, India, sucharithasu@gmail.com BATYRKHAN OMAROV International University of Tourism and Hospitality, Turkistan, Kazakhstan, batyahan@gmail.com SANDEEP KUMAR Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India, sandeep.jaglan@msit.in MOHAMMAD SAJID MOHAMMADI Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia, m.sajid@qu.edu.sa KHALED A. Z. ALYAMANI Applied College, Abqaiq Branch, King Faisal University, P.O. Box 4000, Al-Ahsa 31982, Saudi Arabia, kalyamani@kfu.edu.sa Corresponding Author: Arshiya S. Ansari (ar.ansari@mu.edu.sa) Cyberbullying is a form of abuse, manipulation, or humiliation directed against a single person via the Internet. CB makes advantage of nasty Internet comments and remarks. It occurs when someone publicly mocks, insults, slanders, criticizes, or mocks another person while remaining anonymous on the Internet. As a result, there is a rising need to create new methods for sifting through data on social media sites for symptoms of cyberbullying. The goal is to lessen the negative consequences of this condition. This article discusses a soft computing-based methodology for detecting cyberbullying in social multimedia data. This model incorporates social media data. Normalization is performed to remove noise from data. To improve a feature, the Particle Swarm Optimization Technique is applied. Feature optimization helps to make cyberbullying detection more accurate. The LSTM model is used to classify things. With the help of social media data, the PSO LSTM model is getting better at finding cyberbullying. The accuracy of PSO LSTM is 99.1 percent. It is 2.9 percent higher than the accuracy of the AdaBoost technique and 10.4 percent more than the accuracy of the KNN technique. The specificity and Sensitivity of PSO-based LSTM is also higher in percentage than KNN and AdaBoost algorithm. Keywords- Social Multimedia Data, Cyber Bullying Detection, Feature Optimization, PSO, Denoising, Normalization INTRODUCTION Cyberbullying is a kind of manipulation, humiliation, and abuse that is directed against a specific individual [1]. CB makes use of negative internet postings and messages. It's when one person humiliates, mocks, embarrasses insults, defames, and criticizes