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With a total of 4,156,932,140 internet users by 2017, the number of internet users has increased drastically, reaching 54% of the total population and counting. An increase in the total number of users means more user-generated content across several online platforms, which is predominantly real-time. The user-generated content is being leveraged by applications to derive insights into customer behavior, opinion mining, marketing and for providing niche services like banking in real-time. In recent years, we have also seen a rise in citizen journalism and public posting real-time events on social media channels. Social media has emerged as a supporting player for traditional media as well as powerful standalone expression tool for the public, and hence changing the reliance on traditional media for reports and news. Further, the increase in smartphones and better coverage of data networks has shown increased credible news sourced by mainstream media to be from Social Media. Not only media agencies but the real-time event identification can be used by security departments, disaster management, and others for quick action. The most prominent source of information is the micro-blogging site, Twitter providing geolocation and other features like time, author id, author name, source, link, people’s reaction towards that data, etc. and can be easily extracted, stored and analyzed using Big Data Tools. Entities extraction in Natural language Processing (NLP) is used for identifying the type of event and proceed further. The fundamental goal of our work is to limit the spread of falsehood by halting the proliferation of fake news in the system. This helps us in taking lead in collecting information on certain events ahead of local media platforms. For example, when an earthquake occurs, people make many posts related to the earthquake, which enables detection of earthquake occurrence promptly. Our model delivers such notifications of such events much faster than the announcements of other media sources. In this paper, we have utilized the information from the social platforms in real-time based upon some keywords and geolocation and visualized it with powerful BI tools. Continuous monitoring helps us analyzing the events occurring in the respective geolocation and defining its credibility. The credibility of such an event is detected with the help of the credit score factors developed considering multiple factors including temporal and spatial features of the reported content.
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