Publication Type

Journal Article

Version

submittedVersion

Publication Date

2-2016

Abstract

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.

Keywords

Ranking, Audience segmentation, Social audience, Ensemble learning, Twitter

Discipline

Computer Sciences | Social Media

Research Areas

Data Science and Engineering

Publication

Decision Support Systems

Volume

85

First Page

34

Last Page

48

ISSN

0167-9236

Identifier

10.1016/j.dss.2016.02.010

Publisher

Elsevier

Additional URL

http://dx.doi.org/10.1016/j.dss.2016.02.010

Download

Share

COinS