# Fake news propagate differently from real news even at early stages of spreading

arXiv: https://arxiv.org/abs/1803.03443 · HTML: https://mlsanity.com/paper/1803.03443/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2018.

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

- [Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo](https://mlsanity.com/paper/2205.03068/) (2022)
- [PANACEA: An Automated Misinformation Detection System on COVID-19](https://mlsanity.com/paper/2303.01241/) (2023)
- [Multiverse: Multilingual Evidence for Fake News Detection](https://mlsanity.com/paper/2211.14279/) (2022)
- [SOMPS-Net : Attention based social graph framework for early detection of fake health news](https://mlsanity.com/paper/2111.11272/) (2021)
- [Fake News Quick Detection on Dynamic Heterogeneous Information Networks](https://mlsanity.com/paper/2205.07039/) (2022)
- [Quantitative Analysis of Forecasting Models:In the Aspect of Online Political Bias](https://mlsanity.com/paper/2309.05589/) (2023)

## Abstract (arXiv)

Social media can be a double-edged sword for society, either as a convenient channel exchanging ideas or as an unexpected conduit circulating fake news through a large population. While existing studies of fake news focus on theoretical modeling of propagation or identification methods based on machine learning, it is important to understand the realistic mechanisms between theoretical models and black-box methods. Here we track large databases of fake news and real news in both, Weibo in China and Twitter in Japan from different culture, which include their complete traces of re-postings. We find in both online social networks that fake news spreads distinctively from real news even at early stages of propagation, e.g. five hours after the first re-postings. Our finding demonstrates collective structural signals that help to understand the different propagation evolution of fake news and real news. Different from earlier studies, identifying the topological properties of the information propagation at early stages may offer novel features for early detection of fake news in social media.