A Study on Online News Popularity Prediction Techniques
With the help of Internet, the online news can be instantly spread around the world. Most of peoples now have the habit of reading and sharing news online, for instance, using social media like Twitter and Facebook. Typically, the news popularity can be indicated by the number of reads, likes or shares. For the online news stake holders such as content providers or advertisers, it’s very valuable if the popularity of the news articles can be accurately predicted prior to the publication. With the expansion of the Internet, more and more people enjoy reading and sharing online news articles. The number of shares under a news article indicates how popular the news is. In this project, we intend to study different and the best model and set of features to predict the popularity of online news, using machine learning techniques. The data comes from Mashable, a well-known online news website. Thus, it is interesting and meaningful to use the machine learning techniques to predict the popularity of online news articles. Various works have been done in prediction of online news popularity. Popularity of news depends upon various features like sharing of online news on social media, comments of visitors for news, likes for news articles etc. Feature selection methods are used to improve performance and reduce features So, it is necessary to know what makes one online news article more popular than another article.