Identifying System Location Specifics based on Classification of Worldwide Tweets

As social networking sites are gaining populism across the globe, people are more enthusiastic about sharing their thoughts on Various networking Platforms. Facebook and Twitter have become a leading destination for sharing various kinds of information. In the existing literature the focus is to access the information published in the networking platforms in the real-time, and they do not focus on obtaining the geo-location of the user. Here we propose a monitoring system that classifies the tweets using some reliable techniques which can be used across the globe without any security concerns. As there is a lot of fake news available in the digital form, there is a definite need to access the user information and his geo-location metrics. In this paper, we have introduced Naive Bayes Multinomial classifier and a few other models which performs a spatiotemporal analysis. This study also identifies a comprehensive set of performance metrics which can access the tweet’s country of origin by using eight tweet-inherent features. The outcome of this analysis can be used by various cyber-crime departments to deal with the numerous cybercrime cases on networking platforms, and real-time decisive actions can be taken.