The Design Of A Live Social Observatory System

With the emergence of social networks and their potential impact on society, many research groups and originations are collecting huge amount of social media data from various sites to serve different applications. These systems offer insights on different facets of society at different moments of time. Collectively they are known as social observatory systems. This paper describes the architecture and implementation of a live social observatory system named 'NExT-Live'. It aims to analyze the live online social media data streams to mine social senses, phenomena, influences and geographical trends dynamically. It incorporates an efficient and robust set of crawlers to continually crawl online social interactions on various social network sites. The data crawled are stored and processed in a distributed Hadoop architecture. It then performs the analysis on these social media streams jointly to generate analytics at different levels. In particular, it generates high-level analytics about the sense of different target entitles, including People, Locations, Topics and Organizations. NExT-Live offers a live observatory platform that enables people to know the happenings of the place in order to lead better life.