Advanced computing model for geosocial media using big data analytics

Social media has drastically entered into a new concept by empowering people to publish their data along with their locations in order to provide benefits to the community and the country overall. There is a significant increase in the use of geosocial networks, such as Twitter, Facebook, Foursquare, and Flickr. Therefore, people worldwide can now voice their opinion, report an event instantly, and connect with others while sharing their views. Thus, geosocial network data provides full information on human current trends in terms of behavior, lifestyle, incidents and events, disasters, current medical infections, and much more with respect to location. Hence, current geosocial media can serve as data assets for countries and their government by analyzing geosocial data in a real time. However, there are millions of geosocial network users who generate terabytes of heterogeneous data with a variety of information every day and at high speed; such information is called “Big Data.” Analyzing such a significant amount of data and making real-time decisions regarding event detection is a challenging task. Therefore, in this paper, we propose an efficient system for exploring geosocial networks while harvesting data in order to make real-time decisions while detecting various events. A novel system architecture is proposed and implemented in a real environment in order to process an abundant amount of various social network data to monitor Earth events, incidents, medical diseases, user trends, and views to make future real-time decisions and facilitate future planning. The proposed system consists of five layers, i.e., data collection, data processing, application, communication, and data storage. The system deploys Spark at the top of the Hadoop ecosystem to run a real-time analysis. Twitter and Flickr data are analyzed using the proposed architecture in order to identify current events or disasters, such as earthquakes, fires, Ebola virus contagion, and snow. The system is evaluated on the Tweeter’s data by considering the recent earthquake detection occurred in New Zealand. The system is also evaluated with respect to efficiency while considering system throughput on large datasets. We prove that the system has higher throughput and is capable of analyzing a huge amount of geosocial network data at a real time while detecting any event.