Ranking of high-value social audiences on Twitter
Authors: Lo, Siaw Ling; Chiong, Raymond; Cornforth, David
Journal: Decision Support Systems (2016)
DOI: 10.1016/j.dss.2016.02.010
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 was used in our experiments to validate this ranking mechanism. The results methods is indeed generic enough to retrieve relevant audience members from the three different datasets. 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 datasets 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 decisionmaking.