A Framework for Finding Outspread News Pattern on Diverse Dataset using Time-Series

Abstract

Expressing feeling or opinion is an inherent property of the individual and Now a day’s social media becomes an integral part of everyone’s life. It is a great medium to analyze the feeling of mass, but sometimes it flows the false feeling in the form of fake news or contents posted on social media. These fake content affects the people in the form of sentiments or companies in the business loss/profit, because most of the people make opinions based on what they read on social media. In fact, fake news or false information can create the damage among the individual, so it should be identified as early as possible. The interest in finding the pattern of fake news has been growing very rapidly in the last few years. In this article we proposed a comprehensive pattern analysis of viral contents, real or fake news on twitter using time series analysis. The proposed technique is simple but effective for detecting and analysis fake contents on the social networks. Experiments results shows that our proposed technique outperformed for differentiating real vs fake news on twitter. Finally, we identify and discuss future direction.