Portable Learning Approach towards Capturing Social Intimidating Activities using Big Data and Deep Learning Technologies
- Mansi Mahendru and Sanjay Kumar Dubey
With the rise in the usage of different social media platforms, social intimidation has increasingly spread into these forums as it has given us endless chances to post anything for anyone. Previous studies have confirmed that exposure to this online social intimidation can have very serious offline consequences. With the growth of these multimodal social media platforms, there is an urgent requirement of some device methods for social intimidation detection and prevention. However, most of the prior research has focused on only textual posts for one or two topics of intimidation, namely sexism and racism. The principal objective of this research is to recognize social intimidation from multimodal posts such as text, memes, videos and audio and to target various social media networks such as Instagram, Twitter, and Facebook for several topics of harassment, namely religion based, personal attack, racism, sexism, physical appearance, etc. Previous research has stopped at detection, but this research has taken one step ahead to test the severity based on hate prediction score. The research study is performed using a combination of big data technology, namely Apache Spark, and several deep learning methods which are described below. The system is validated on five public datasets i.e., MLMA Hate Speech Dataset, MMHS150K Dataset, Hateful Memes Dataset, Instagram, Vine Dataset and measured on the basis of precision, recall and f1-score. Performance of the system has been inspected individually for every category of post under three subsections. The results attained specify that the proposed approach gives more feasible solution for social intimidation detection and its severity in online social networking platforms.