# Searching for superspreaders of information in real-world social media

arXiv: https://arxiv.org/abs/1405.1790 · HTML: https://mlsanity.com/paper/1405.1790/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2014.

## Built on by (most cited first, 6 of 6)

- [The Spread of Propaganda by Coordinated Communities on Social Media](https://mlsanity.com/paper/2109.13046/) (2021)
- [Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence](https://mlsanity.com/paper/2301.06774/) (2023)
- [Human-interpretable clustering of short-text using large language models](https://mlsanity.com/paper/2405.07278/) (2024)
- [Edge Ranking of Graphs in Transportation Networks using a Graph Neural Network (GNN)](https://mlsanity.com/paper/2303.17485/) (2023)
- [GNN-Based Candidate Node Predictor for Influence Maximization in Temporal Graphs](https://mlsanity.com/paper/2503.23713/) (2025)
- [Multimodal Coordinated Online Behavior: Trade-offs and Strategies](https://mlsanity.com/paper/2507.12108/) (2025)

## Abstract (arXiv)

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. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.