Analyzing and Ranking Prevalent News over Social Media

Abstract

Since the growth of social media, news outrage over mass through social media has become evident and its control over all information sources has increased significantly. Social media services such as twitter collects enormous amounts of information and allows media companies to publish news related information as tweets. Every other big news company has its twitter account to public news in form of tweets. Beside news social media platforms has enormous amount of news attached to them as well. To make correct and better information reach to users we have to filter noise and segregate the content based on similarity and content’s respective value. Even after filtering noise, information payload exists in data so to prioritize information must be ranked in order of considered factors. In our proposed work, news are filtered and ranked based on three factors. First, media focus (MF) which tells the temporal prevalence of a particular topic in news media. Second, user attention (UA) which tells how mass is responding to the topic. Last, is the user interaction which tells how users are forming view over the topic. Our proposed work introduces an unsupervised machine learning framework which identifies news topics prevalent in both social media and the news media, and then ranks them ordering them using their degrees of MF, UA, and UI.