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PLoS One. 2018 Mar 20;13(3):e0194192. doi: 10.1371/journal.pone.0194192. eCollection 2018.

Mention effect in information diffusion on a micro-blogging network.

Author information

1
School of Software Engineering, Beijing Jiaotong University, Beijing, China.
2
CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
3
CompleX Lab, Web Sciences Center and Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
4
Center for Complex Network Research, Northeastern University, Boston, MA, United States of America.
5
China Information Technology Security Evaluation Center, Beijing, China.

Abstract

Micro-blogging systems have become one of the most important ways for information sharing. Network structure and users' interactions such as forwarding behaviors have aroused considerable research attention, while mention, as a key feature in micro-blogging platforms which can improve the visibility of a message and direct it to a particular user beyond the underlying social structure, is seldom studied in previous works. In this paper, we empirically study the mention effect in information diffusion, using the dataset from a population-scale social media website. We find that users with high number of followers would receive much more mentions than others. We further investigate the effect of mention in information diffusion by examining the response probability with respect to the number of mentions in a message and observe a saturation at around 5 mentions. Furthermore, we find that the response probability is the highest when a reciprocal followship exists between users, and one is more likely to receive a target user's response if they have similar social status. To illustrate these findings, we propose the response prediction task and formulate it as a binary classification problem. Extensive evaluation demonstrates the effectiveness of discovered factors. Our results have consequences for the understanding of human dynamics on the social network, and potential implications for viral marketing and public opinion monitoring.

PMID:
29558498
PMCID:
PMC5860736
DOI:
10.1371/journal.pone.0194192
[Indexed for MEDLINE]
Free PMC Article

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