Cyberbullying in text content detection: an analytical review

AbstractTechnological advancements have resulted in an exponential increase in the use of online social networks (OSNs) worldwide. While online social networks provide a great communication medium, they also increase the user's exposure to life-threatening situations such as suicide, eating disorder, cybercrime, compulsive behavior, anxiety, and depression. To tackle the issue of cyberbullying, most existing literature focuses on developing approaches to identifying factors and understanding the textual factors associated with cyberbullying. While most of these approaches have brought great success in cyberbullying research, data availability needed to develop model detection remains a challenge in the research space. This paper conducts a comprehensive literature review to provide an understanding of cyberbullying detection.Keywords: Cyberbullyingcybercrimetext detectiondeep learningsocial media Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSylvia W. AzumahSylvia W. Azumah is a Ghanaian PhD candidate in Information Technology, specializing in cybersecurity at the University of Cincinnati. She holds a master's in IT from the same university, where she was recognized as the most outstanding student and a bachelors in IT at Bluecrest University from Ghana, West Africa An avid coder, mentor to women in cybersecurity.Nelly ElsayedDr. Nelly Elsayed is an Assistant Professor at the School of Information Technology. She is the Leader and Founder of the Applied Machine Learning and Intelligence Lab. She received a BS. and MS. degree in Computer Science from Alexandria University, and she received her MS. Eng. and Ph.D. degrees from the University of Louisiana at Lafayette. She is an IEEE Computational Intelligence Society active member. She has served as a principal investigator and co-principle investigator in different federal, educational, and industrial level-funded research projects. She received the Faculty Incentive Award for Research and Scholarship from the CECH, UC, recognizing her research contributions, journal and conference peer-reviewed publications, and professional presentations in 2020-2021. She received the Love of Learning Award from the Honor Society Phi Kappa Phi in 2019, 2021 and 2023. She received the Golden Apple Award for Excellence in Teaching (Graduate Level), CECH. She received the UCAADA Sarah Grant Barber Outstanding Advising Faculty Award for the academic year 2021-2022 University of Cincinnati. She has been an Ambassador for Goodwill of Lafayette, Louisiana, since 2017.Zag ElSayedDr Zag ElSayed was born in Odessa, USSR; he is a computer engineering scientist specializing in the Brain Machine Interface, Artificial Intelligence, Cybersecurity for Cyber-Physical Systems and I2oT as well as VLSI Digital Design. He received his B.S. and M.S. with Distinction degree of Honor from Alexandria University in 2005 where he introduced the early framework architecture for Industrial Internet of things (IoT) implementation. He got his second M.Sc. and Ph.D. in Computer Engineering from the University of Louisiana at Lafayette in 2016; he worked as a research engineer in Egypt, Russia, Ukraine, and the USA. Since 2014, he has been a constant for leading oil and gas companies specializing in Industrial IoT development and implementation. He is fluent in nine languages, a nationally recognized painting artist, and a registered Red Cross volunteer. Zag believes the key to understanding the Universe is ciphered in the human brain. This talk was given at a TEDx event using the TED conference format at https://www.ted.com/tedx.Murat OzerMurat Ozer, Ph.D. is an Assistant Professor in the School of Information Technology at the University of Cincinnati. part of the university's College of Education, Criminal Justice, and Human Services. He leads the development of the corrections chatbot which will use curated information to offer a new resource for the criminal justice system.