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.

本文探究通过信息传播模式判断未经证实谣言真伪的有效性，并使用图形核从Twitter级联结构中提取复杂的拓扑信息，训练出具有高准确性的预测模型。结果表明，团体的共享模式可以揭示谣言的真相或虚假性，即使在谣言传播的早期阶段。

基于扩散模式的Twitter谣言真实度判定