The Strength of Structural Diversity in Online Social Networks
Name
9831621.pdf
Description
Published version
Size
1.74 MB
Format
Adobe PDF
Checksum (MD5)
43ac99b83a6a3bd6f7b5fc5a2f3d0f0b
Author(s)
Zhang, Yafei
Wang, Lin
Zhu, Jonathan JH
Wang, Xiaofan
Pentland, Alex Sandy’
Date Issued
2021
Journal
Research
Publisher
American Association for the Advancement of Science (AAAS)
Citation
Zhang, Yafei, Wang, Lin, Zhu, Jonathan JH, Wang, Xiaofan and Pentland, Alex Sandy’. 2021. "The Strength of Structural Diversity in Online Social Networks." Research, 2021.
Version
Final published version
Abstract
Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective outcomes. Leveraging a large-scale dataset from a knowledge-sharing website, this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one’s online social reputation. To capture the structural diversity of an individual, we first consider the number of weakly and strongly connected components in one’s contact neighborhood and further take the coexposure network of social neighbors into consideration. We show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation, and the inclusion of a coexposure network provides an additional ingredient to achieve that goal. After synthetically controlling several possible confounding factors through matching experiments, structural diversity still plays a nonnegligible role in the prediction of personal online social reputation. Our work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains, such as social influence and collective intelligence studies.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.34133/2021/9831621
