The use of social media has become an increasingly popular trend, and it is most favorite amongst teenagers. A major problem concerning teens using social media is that they are often unaware of the dangers involved when using these media. Also, teenagers are more inclined to misuse social media because they are often unaware of the privacy rights associated with the use of that particular media, or the rights of the other users. As a result, cyberbullying cases have a steady rise in recent years and have gone undiscovered, or are not discovered until serious harm has been caused to the victims. This study aims to create an effective algorithm that can be used to detect cyberbullying in social media using content mining. Bullies may not use only one social media to victimize other users. Therefore, the proposed algorithm must detect whether or not a user is victimizing someone through one or more social media accounts, then determine which social media accounts are being used to carry out the victimization. To achieve this goal, the algorithm will collect information from content shared by the users in all of their social media accounts, then will determine which content to extract based on a big data technology involving phrases or words that might be used by cyberbullies. Any extracted data will reveal some insight into whether or not cyberbullying is occurring and trigger appropriate approaches to handle it.
Shawniece L. Parker and Yen-Hung Hu. 2016. Content Mining Techniques for Detecting Cyberbullying in Social Media. Virginia Journal of Science 67 (3/4): 12pp. Online ahead of print. doi: 10.25778/WHAA-RK11 Available at: https://digitalcommons.odu.edu/vjs/vol67/iss3/1