Ranking of high-value social audiences on Twitter

Even though social media offers plenty of business opportunities, for a company to identify the right audience from the massive amount of social media data is highly challenging given finite resources and marketing budgets. In this paper, we present a ranking mechanism that is capable of identifying the top-k social audience members on Twitter based on an index. Data from three different Twitter business account owners were used in our experiments to validate this ranking mechanism. The results show that the index developed using a combination of semi-supervised and supervised learning methods is indeed generic enough to retrieve relevant audience members from the three different data sets. This approach of combining Fuzzy Match, Twitter Latent Dirichlet Allocation and Support Vector Machine Ensemble is able to leverage on the content of account owners to construct seed words and training data sets with minimal annotation efforts. We conclude that this ranking mechanism has the potential to be adopted in real-world applications for differentiating prospective customers from the general audience and enabling market segmentation for better business decision making

Ranked Content Advertising in Online Social Networks

Online social networks (OSNs) such as Twitter, Digg and Facebook have become popular. Users post news, photos and videos, etc. and followers of such users then view and comment the posted information. In general, we call the users who produce the information as the information producers, and the users who view the information as the information consumers. The recently popular targeted information advertising systems enable the producers to target users (i.e., consumers). A key problem of the dvertising system is to efficiently find the top-k most desirable targeted users, who next will view the advertised information and perform potential e-commerce activities. Unfortunately, state-of-the-art solutions to find the top-k desirable targeted users in large OSNs incur high space cost and slow running time. In this paper, we focus on designing efficient algorithms to overcome such efficiency issues. Experimental results, over synthetic and real data sets, demonstrate the effectiveness and efficiency of our algorithms