A quantitative methodology to identify relevant users in social networks

Social networks are gaining an increasing popularity on the Internet, with tens of millions of registered users and an amount of exchanged contents accounting for a large fraction of the Internet traffic. Due to this popularity, social networks are becoming a critical media for business and marketing, as testified by viral advertisement campaigns based on such networks. To exploit the potential of social networks, it is necessary to classify the users in order to identify the most relevant ones. For example, in the context of marketing on social networks, it is necessary to identify which users should be involved in an advertisement campaign. However, the complexity of social networks, where each user is described by a large number of attributes, transforms the problem of identifying relevant users in a needle in a haystack problem. Starting from a set of user attributes that may be redundant or do not provide significant information for our analysis, we need to extract a limited number of meaningful characteristics that can be used to identify relevant users. We propose a quantitative methodology based on Principal Component Analysis (PCA) to analyze attributes and extract characteristics of social network users from the initial attribute set. The proposed methodology can be applied to identify relevant users in social network for different types of analysis. As an application, we present two case studies that show how the proposed methodology can be used to identify relevant users for marketing on the popular YouTube network. Specifically, we identify which users may play a key role in the content dissemination and how users may be affected by different dissemination strategies.