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• W2577776871 abstract "Nowadays, social media has become a popular platform for companies to understand their customers. It provides valuable opportunities to gain new insights into how a person’s opinion about a product is influenced by his friends. Though various approaches have been proposed to study the opinion formation problem, they all formulate opinions as the derived sentiment values either discrete or continuous without considering the semantic information. In this paper, we propose a Content-based Social Influence Model to study the implicit mechanism underlying the change of opinions. We then apply the learned model to predict users’ future opinions. The advantages of the proposed model is the ability to handle the semantic information and to learn two influence components including the opinion influence of the content information and the social relation factors. In the experiments conducted on Twitter datasets, our model significantly outperforms other popular opinion formation models." @default.
• W2577776871 created "2017-01-26" @default.
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• W2577776871 date "2016-01-01" @default.
• W2577776871 modified "2023-09-24" @default.
• W2577776871 title "Content-based influence modeling for opinion behavior prediction" @default.
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