A 1-Gram Sentiment Analysis Algorithm for Detecting Cyberbullying in Online Social Networks

Abstract

Online social networking (OSN) sites in addition to providing business and recreational opportunities are fast becoming a breeding ground for cyberbullying activities. Cyberbullying is an act of harassing or insulting a person by sending messages that are hurting or threatening in nature using electronic communication. Such messages include threats, harassment, and humiliating messages to victims. Other forms are sexual harassments, sexual predating, etc. Cyberbullying poses threat to the physical and mental health of the victims. In this study, sentiments analysis was used to computationally recognize and categorize the opinions, views, and ideas expressed in a piece of text in social media to determine and establish whether the writer's attitude towards a particular topic, person, or a product is positive or negative. The study adopted both quantitative and qualitative approaches. Posts from Facebook were collected and analyzed. The software developed during the research was able detect the presence of cyberbullying in user contents. Results showed promising ability of the software to detect and suspend cyberbullying contents.