Abstract
Epidemic dynamics in complex networks have been extensively studied. Due to the similarity between information and disease spreading, most studies on information dynamics use epidemic models and merely consider the characteristics of online social networks and individual’s cognitive. In this paper, we propose an online social networks information spreading (OSIS) model combining epidemic models and individual’s cognitive psychology. Then we design a cellular automata (CA) method to provide a computational method for OSIS. Finally, we use OSIS and CA to simulate the spreading and evolution of information in online social networks. The experimental results indicate that OSIS is effective. Firstly, individual’s cognition affects online information spreading. When infection rate is low, it prevents the spreading, whereas when infection rate is sufficiently high, it promotes transmission. Secondly, the explosion of online social network scale and the convenience of we-media greatly increase the ability of information dissemination. Lastly, the demise of information is affected by both time and heat decay rather than probability. We believe that these findings are in the right direction for perceiving information spreading in online social networks and useful for public management policymakers seeking to design efficient programs.
Highlights
Networks such as Internet, communication networks, and social networks can be used to describe the interconnections among individuals
We demonstrate that the explosion of online social network scale and the convenience of we-media can greatly increase the ability of information dissemination, especially rumors
Since information spreading is similar to epidemic, we propose our online social network information spreading (OSIS) model by combining the characteristics of online social networks mentioned above with epidemic SEIR model
Summary
Networks such as Internet, communication networks, and social networks can be used to describe the interconnections among individuals. According to the degree of each network node, complex networks are divided into regular networks, Erdos-Renyi (ER) networks [5], small-world networks [6], and scale-free (SF) networks [7] Among those networks, the small-world model proposed by Watts and Strogatz describes the features of high clustering and small average path length, which is most suitable for real social networks. The small-world model proposed by Watts and Strogatz describes the features of high clustering and small average path length, which is most suitable for real social networks These small-world features [8] have been found to have a significant impact on the dynamics in networks [9]. Due to the similarity between information and disease, the models mentioned above are widely considered
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