Document Type
Article
DOI
10.25778/WHAA-RK11
Abstract
The use of social media has become an increasingly popular trend, primarily among teenagers. A major problem concerning teens using social media is that they are often unaware of the dangers involved when using these media. They are also more inclined to misuse social media because they are often unaware of the privacy rights associated with their use, or the rights of other users. As a result, cyberbullying cases have steadily risen in recent years, often going undiscovered or not being 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 platform 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 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.
Recommended Citation
Parker, S. L. and Y-H. Hu. 2016. Content Mining Techniques for Detecting Cyberbullying in Social Media. Virginia Journal of Science, 67(3/4): 1-11. doi: 10.25778/WHAA-RK11. Available at: https://digitalcommons.odu.edu/vjs/vol67/iss3/1
"online ahead of print" version
Comments
This article has been copyedited and reformatted and is now in its final version. The early “online ahead of print” version is available for reference as an "Additional File" (below).