User Behavior Analysis Of Social TV

With the development of information technology, especially the launch of WEB 2.0 and the popularity of network TV, more and more users exploit the Internet to get information, and published public opinion online on social, economic, political, and other aspects. However, the Internet brings not convenience but also safety problem. On the Internet, the user can more easily grouped, distort the facts, and become propellent of some sensitive topics, so is especially important to analysis and identify the behavior oInternet user. In the paper we analyze the user behavior of social TV based on Microblog and P2P TV dataset. The main content is:First, the Microblog user behavior analysis.The content mainly includes two parts: key character behavior analysis and active user behavior analysis. The former mainly focused on the node user who have lots of network fans and played a key role in the information broadcast. Including the fans'geographical location distribution analysis and public attention and active degrees analysis. The later focused on some certain event and the users whose post and transmit number were on the top.Second, the network TV user behavior analysis. The main content included the user's online time distribution analysis, online hour based user classification analysis, the online hour evolution of classificated user, the network TV user geographical location evolution analysis, user arrival rate and other user behavior analysis. Those four analysis all based on the date of Hunan Satellite TV Channel. The distribution of user online hour described the continue watching duration, reflected user'loyalty to Hunan Satellite TV Channel. According to the length of online hour this paper divided network TV user into three kinds: the mild viewer, moderate viewer and severe viewer. It also reflected the distribution of Hunan Satellite TV Channel user in different time point.The network TV user geographical location evolution is using GoogleMaps API to node the user location in the map. At last, the article studied the user arrival rate.Third, the comparison and analysis of Microblog platform user behavior and P2P TV platform user behavior.Mainly included the number of online users, the distribution of user online hour, the online hour evolution and the geographic distribution.And analysis certain event to shoe the difference and similarity of those two network platform.