Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Threshold models for competitive influence in social networks
WINE'10 Proceedings of the 6th international conference on Internet and network economics
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The polarity of opinion is a crucial part of information and ignoring the asymmetry between them, can potentially result in an inaccurate estimation of the number of product adoptions and incorrect recommendations. We analyze the propagation patterns of the negative and positive opinions on two real world datasets, Flixster and Epinions, and observe that the presence of negative opinions significantly reduces the number of expressed opinions. To account for the asymmetry between the two kind of opinions, we propose extensions of the two most popular information propagation models, Independent Cascade and Linear Threshold models. The proposed extensions give a tractable influence problem and improves the prediction accuracy of future opinions, by more than 3% on Flixster and 5% on Epinions datasets.