Measuring influence in social networks using a network amplification score - an analysis using cloud computing

Information shared on social networks can be attributed to various factors such as common interests, innovation in areas of job role, etc. The propagation of information or its reach in a social network is directly related to the influence of the author. Traditional influence scoring algorithms rely only on the number of direct connections of a user or on a ratio of their connections. As proved by recent research, a focus solely on the connection between users skews an understanding of how influence operates and follows. In this paper we differentiate between passive and active information. Information is classified as active if it promotes engagement through conversation and is thus exposed to more members, increasing the reach of information. Using a large amount of social network data we analyze the different components of information generated by a user, its propagation within the network and derive a metric for calculating the user´s influence score. This influence score considers both the passive content and the active conversation aspect of the shared information. The score relies on the network metric degree of centrality. The measure of inbound and outbound information for 2 levels in the network, provide insight to the amplification of the reach of the shared information.