Abstract
The rapid development of World Wide Web accelerates information spreading in various ways. Thanks to the emergence of multiple social platforms, some events which are not much attractive in the past can become social hot spots nowadays. In this paper, we study the information diffusion process of “IP MAN3 box office fraud”, which is widely diffused in the largest Chinese microblogging system, namely Sina Weibo, in March 2016. Based on the temporal metric we have proposed, we succeed in finding out the sources of the information, and constructing the panorama of the diffusion process. In addition, a portion of nodes that promote the diffusion are identified by using the node importance algorithms. Finally, the users with abnormal behaviors in the process of event development are identified.
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Acknowledgments
This work was partially supported by Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LR18A050001, LY18A050004 and LQ16F030006), Natural Science Foundation of China (Grant Nos. 61673151 and 11671241) and the EUFP7 Grant 611272 (project GROWTHCOM).
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Appendix
Appendix
Abnormal users information | ||
|---|---|---|
Anonymous ID | Anonymous name | Type |
97394430 | Media 5 | Official medias |
699174196 | Media 10 | |
731638928 | Media 11 | |
853079662 | Media 12 | |
708870469 | Media 13 | |
617383185 | Media 14 | |
612128353 | Media 4 | |
119305779 | Media 15 | |
206755762 | Enterprise user 1 | Enterprise users |
197819660 | Enterprise user 2 | |
124872340 | Enterprise user 3 | |
144799753 | Enterprise user 4 | |
210073340 | Enterprise user 5 | |
735707880 | Enterprise user 6 | |
657976480 | Enterprise user 7 | |
666111015 | Enterprise user 8 | |
200485887 | Enterprise user 9 | |
194044453 | Enterprise user10 | |
207588018 | Enterprise user11 | |
203921855 | Enterprise user12 | |
784250235 | Enterprise user13 | |
209345803 | Enterprise user14 | |
654156459 | We Media2 | We media |
609509321 | We Media 4 | |
786955035 | Columnist2 | Columnist |
512500175 | Columnist3 | |
505128349 | Ordinary user 17 | Ordinary user |
205250357 | Ordinary user 18 | |
213261996 | Ordinary user 19 | |
665988504 | Ordinary user 20 | |
441603440 | Ordinary user 21 | |
657288882 | Ordinary user 22 | |
202591342 | Ordinary user 23 | |
803846391 | Ordinary user 24 | |
611645649 | Ordinary user 25 | |
110999748 | Ordinary user 26 | |
772151948 | Ordinary user 27 | |
816024305 | Ordinary user 28 | |
202048903 | Ordinary user 29 | |
806147150 | Ordinary user 8 | |
701165025 | Ordinary user 30 | |
707565922 | Ordinary user 14 | |
116682933 | Ordinary user 31 | |
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Zhou, N., Zhan, XX., Ma, Q., Lin, S., Zhang, J., Zhang, ZK. (2018). Identifying Spreading Sources and Influential Nodes of Hot Events on Social Networks. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_76
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DOI: https://doi.org/10.1007/978-3-319-72150-7_76
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