Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
FRINGE: a new approach to the detection of overlapping communities in graphs
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III
W-entropy index: the impact of the members on social networks
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part I
Analysis of social metrics in dynamic networks: measuring the influence with FRINGE
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Who will follow whom? exploiting semantics for link prediction in attention-information networks
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
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With the rapid advance of the social media, the challenge is to develop new techniques and standards to measure the influence of people or brands in the online social networks. Each website has its way of ranking the display of the most influential members of its virtual society. However, most of the current measurement methods are incomplete and one-dimensional. This paper presents a new measurement model, W-entropy, which has been developed based on information theory to study the influence of individuals based on different social networks. The model was tested using data from Facebook, Twitter, YouTube, and Google search. The proposed model can be extended to other platforms. To evaluate the effectiveness, the developed method was compared with Famecount ranking using the same data with different weight distributions. The result shows that W-entropy method is suitable for index ranking to reflect uneven information distribution across various social networks.