Potential Influencers Identification Using Multi-Criteria Decision Making (MCDM) Methods

Multi-Criteria Decision Making (MCDM) methods such as Pugh, technique for order preference by similarity to an ideal solution (TOPSIS) are discussed to identify the potential influencers in Social Media.. In Web 2.0 technology, every user is become major contributors in online social media. Social m...

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Bibliographic Details
Published in:Procedia computer science 2015, Vol.57, p.1179-1188
Main Authors: Gandhi, Meera, Muruganantham, A.
Format: Article
Language:English
Subjects:
Facebook
Influence Users
Pugh technique
Social Influence
Social Network
TOPSIS
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Summary:Multi-Criteria Decision Making (MCDM) methods such as Pugh, technique for order preference by similarity to an ideal solution (TOPSIS) are discussed to identify the potential influencers in Social Media.. In Web 2.0 technology, every user is become major contributors in online social media. Social media sites like Facebook, Twitter are common phenomena for business organizations to offer business services to their customers. The amount of interaction generated at these social media sites are in the form like post, comments, Tweets, likes etc. influences the attitude and behaviour of others. It is important to monitor, estimate and engage the potential influencers who are most relevant to the brand, product or campaign are become important now. In this way, business enterprises could retain efforts aimed at sustaining the activity of influential users, who take minimal effort and resources to improve product sales and enhance their reputations to improve the business enterprise. In this article, a research framework was proposed to estimate the influencers in a social media site using Multi-criteria Decision Making (MCDM) methods and compared the results. The proposed approach is more dynamic and capable of identifying the potential influencers preciously than using standard centrality measures, which are incapable to be applied in large-scale networks due to the computational complexity. The MCDM based approach effectiveness was tested using a Facebook datasets and the results were shown with existing algorithms such as degree, PageRank and centrality measures. Comparisons were made on the ranking of influencers to evaluate the performance of MCDM methods, in which TOPSIS outperforms other methods.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2015.07.411