STaR System: Detecting Legitimate Information by Identifying and Prioritizing Prevalent News

Mass media such as News media gives us day to day updates about the events happening around the world. Social media is one of the most popular means of communication on the internet. Twitter is one among them which provides large amount of user generated data, which contain information related to news. In this work, a new technique is proposed to filter noise and to obtain the data which is similar in the news media, which is considered to be valuable. However, some noise may still be present in the remaining data, so it is necessary to prioritize it for consumption. Prioritization can be achieved by ranking the information using three factors, firstly, media focus which determines whether the same news has come in the news media or not. Secondly, user attention which tells how many people have shown interest to that particular news. And finally, user interaction where the interests and comments of the user in social media are considered. Hence the proposed work identifies and ranks news topic.