LAUSR

Abstract Social networks have involved more and more users who search for and share information extensively and frequently. Tremendous evidence in Facebook, Twitter, Flickr and Google+ alike shows that such social networks are the major information sources as well as the most effective platforms for information transmission and exchange. The dynamic propagation of various information may gradually disseminate, drastically increase, strongly compete with each other, or slowly decrease. These observations had led to the present study of the spreading process of true and fake information over social networks, particularly the Facebook. Specifically, in this paper the topological structure of two huge-scale Facebook network datasets are investigated regarding their statistical properties. Based on that, an information model for simulating the true and fake information spreading over the Facebook is established. Through controlling the spreading parameters in extensive large-scale simulations, it is found that the final density of stiflers increases with the growth of the spreading rate, while it would decline with the increase of the removal rate. Moreover, it is found that the spreading process of the true–fake information is closely related to the node degrees on the network. Hub-individuals with high degrees have large probabilities to learn hidden information and then spread it. Interestingly, it is found that the spreading rate of the true information but not of the fake information has a great effect on the information spreading process, reflecting the human nature in believing and spreading truths in social activities. The new findings validate the proposed model to be capable of characterizing the dynamic evolution of true and fake information over the Facebook, useful and informative for future social science studies.