Publication

Abstract : Information technology has been improvising day by day. Key reason is that the usage of internet is increasing every year. This increases the data size as per the wiki statistics, majority of the people are interested in using social medias, thus the social media especially like Facebook and Twitter has lots audience all over the world. Controlling and validating the information source in these social media are really toughest challenge. Recently social media is used as a platform to spread the rumor and fake information such as morphed videos and images that affects social status of many innocent victims. There are also many people who lost their life due to the social media rumors. Identifying and reporting the rumor source before it spreads virally is the toughest challenge in current social media sites. In this paper, we have proposed an architecture and design to identify the rumor sources using machine learning model. Our base idea is to collect the common features vectors from various fake news source dataset. Then we used machine learning algorithms to train our model to predict the rumor news. We have implemented and tested our model with twitter dataset for various cases along with empirical results