Opinion influence and diffusion in social network

Nowadays, more and more people tend to make decisions based on the opinion information from the Internet, in addition to recommendations from offline friends or parents. For example, we may browse the resumes and comments on election candidates to determine if one candidate is qualified, or consult the consumer reports or reviews on special e-commercial websites to decide which brand of computer is suitable for one's needs. Though opinion information is rich on the Internet, [2] points out that 58% of American Internet users deem that online information is irretrievable, confusing, or conflicting with each other. Early works on opinion mining help to classify opinion polarity, to extract specific opinions and to summarize opinion texts. However, all these works are usually based on plain texts (reviews, comments or news articles). With the explosion of Web 2.0 applications, especially social network applications like blogs, discussion forums, micro-blogs, the massive individual users go to the major media websites, which leads to much more opinion materials posted on the Internet by user-shared experiences or views [3]. These opinion-rich and social network-based applications bring new perspectives for opinion mining as well. First, in addition to plain texts (reviews, newswire) in traditional opinion mining, we see new types of cyber-based text, like personal diary blogs, cyber-SMS tweets. Second, if we regard the opinions in plain text as static, the dynamic change of opinions in the social network is a new promising area, and catch increasing attention of worldwide researchers. In the social network, the opinion held by one individual is not static, but changes, which can be influenced by others. A serial of changes among different users forms the opinion propagation or diffusion in the network.

This paper and my doctoral work focus on the opinion influence and diffusion in the social network, which explore the detailed process of one-to-one influence and the opinion diffusion process in the social network. The significance of this work is it can benefit many other related researches, like information maximum, viral marketing. Now some pioneering works have been conducted to investigate the role of social networks in information diffusion and influencers in the social network. These works are usually based on information diffusion models, like the cascade model (CM) or epidemic model (EM). However, we argue that it is not enough to simply apply these models to opinion influence and diffusion. 1) For both CM and EM, status shift is along specific directions, from inactive to active (CM) or from susceptible to infectious, and then, to recovered (EM). But opinion influence is more complex.