Searching for superspreaders of information in real-world social media

@inproceedings{Pei2014SearchingFS,
  title={Searching for superspreaders of information in real-world social media},
  author={Sen Pei and Lev Muchnik and Jos{\'e} S. Andrade and Zhiming Zheng and Hern{\'a}n A. Makse},
  booktitle={Scientific reports},
  year={2014}
}
A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 120 CITATIONS

Weighted kshell degree neighborhood method: An approach independent of completeness of global network structure for identifying the influential spreaders

  • 2018 10th International Conference on Communication Systems & Networks (COMSNETS)
  • 2018
VIEW 4 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Estimating influence of social media users from sampled social networks

  • 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
  • 2016
VIEW 8 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Analysis of the Evolution of the Influence of Central Nodes in a Twitter Social Network

  • 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)
  • 2019
VIEW 4 EXCERPTS
CITES RESULTS, BACKGROUND & METHODS
HIGHLY INFLUENCED

Identifying opinion leaders in social networks with topic limitation

  • Cluster Computing
  • 2017
VIEW 4 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

A comparison of methods for cascade prediction

  • 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
  • 2016
VIEW 3 EXCERPTS
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2014
2019

CITATION STATISTICS

  • 9 Highly Influenced Citations

  • Averaged 24 Citations per year from 2017 through 2019