Analysing and Identifying Crucial Evidences for the prediction of False Information proliferated during COVID-19 Outbreak: A Case Study

In the current scenario social media platforms are one the efficient way to share opinions and thoughts of an individual. User can freely share their thoughts on an event/ situation. This can be a curse for the society if social media platform is utilized with some bad intention to spread false information and create chaos/ confusion among public which greatly degrades user experience. In the current pandemic many people have their eye on any news article related to corona cure. Malicious users take this as an opportunity to spread fake news in order to create confusion among public or some monetary benefits, the detection of which is of paramount importance. The proposed technique is leverages to learn crucial evidences based on Context Knowledge, Distance Metric and Word Resemblance with respect to news article headline and its content concerning top 10 google search results related to the claim, where considering COVID-19 as one of the special case studies from the application perspective. This paper proposed a novel scheme for the prediction of false information and generated a covidfakenews dataset that further be utilized for the analysis and evaluation of our model. The results reveals that the proposed intelligent strategy gives promising experimental results and quite effective in predicting False information.