# A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone

arXiv: https://arxiv.org/abs/2002.00850 · HTML: https://mlsanity.com/paper/2002.00850/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 12 of 12)

- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2112.10322/) (2021)
- [A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning](https://mlsanity.com/paper/2204.02626/) (2022)
- [Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo](https://mlsanity.com/paper/2205.03068/) (2022)
- [Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets](https://mlsanity.com/paper/2104.05321/) (2021)
- [Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models](https://mlsanity.com/paper/2406.02143/) (2024)
- [MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection](https://mlsanity.com/paper/2210.05401/) (2022)
- [Rumor Detection with Diverse Counterfactual Evidence](https://mlsanity.com/paper/2307.09296/) (2023)
- [Recurrent Graph Neural Networks for Rumor Detection in Online Forums](https://mlsanity.com/paper/2108.03548/) (2021)
- [Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator](https://mlsanity.com/paper/2405.13094/) (2024)
- [Leveraging Social Interactions to Detect Misinformation on Social Media](https://mlsanity.com/paper/2304.02983/) (2023)
- [Detecting Rumor Veracity with Only Textual Information by Double-Channel Structure](https://mlsanity.com/paper/2312.03195/) (2023)

## Abstract (arXiv)

Recent work in the domain of misinformation detection has leveraged rich signals in the text and user identities associated with content on social media. But text can be strategically manipulated and accounts reopened under different aliases, suggesting that these approaches are inherently brittle. In this work, we investigate an alternative modality that is naturally robust: the pattern in which information propagates. Can the veracity of an unverified rumor spreading online be discerned solely on the basis of its pattern of diffusion through the social network? Using graph kernels to extract complex topological information from Twitter cascade structures, we train accurate predictive models that are blind to language, user identities, and time, demonstrating for the first time that such "sanitized" diffusion patterns are highly informative of veracity. Our results indicate that, with proper aggregation, the collective sharing pattern of the crowd may reveal powerful signals of rumor truth or falsehood, even in the early stages of propagation.