Abstract
Cyberbullying has grown as an important societal challenge nowadays. The Cyberbullying affects both in terms of psychological and emotional means of a person. So there is a need to devise a method to detect and prevent cyberbullying in social networks. Most of the existing cyberbullying methods involves only text detection and few methods are available for analysing the visual detection. In this proposed work is going to detect multimodel cyberbullying such as audio, video, image along with text in the social networks. The cyberbully image will be detected using the computer vision algorithm which includes two methods like Image Similarity and Optical Character Recognition (OCR). The cyberbully video will be detected using the Shot Boundary detection algorithm where the video will be broken into frames and analysed using various methods in it. The proposed framework also support to identify the cyberbully audio in the social network. Finally the cyberbully data will be classified into Physical bullying, Social bullying and Verbal bullying using classifiers.
Highlights
The Social networking platform has become very popular in the last few years
The audio will be converted into text using CMU Sphinx tool.In the converted text cyberbully will be detected using trained dataset [17]. In this module the cyberbully data will be classified into Physical bullying, Social bullying and Verbal bullying using Naïve Bayesian classifier
The new ideology in the method of bullying that decreases into this grouping involves boosting others to circumvent a definite person or group.Social bullying affects a person and their ability to relate to their environment as well as other people in a social setting
Summary
The Social networking platform has become very popular in the last few years. Twitter has on average 500 million tweets per day, while Facebook remains the largest social media network with million active users and many million people sending updates. As this shows, cybercrime criminals have been using both platforms which have attracted thousands of views, comments, forums and posts. Through the use of videos, images posted on YouTube, Facebook and Instagram they try bully people. The main objective of this proposed work is to detect the cyberbully content in different forms such as image, audio, video and text in the social media.
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