论文标题
真理的内核：仅通过扩散模式来确定Twitter上的谣言真实性
A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone
论文作者
论文摘要
错误信息检测领域的最新工作已利用与社交媒体上内容相关的文本和用户身份中的丰富信号。但是文本可以在战略上操纵，并在不同的别名下重新开放，这表明这些方法本质上是脆弱的。在这项工作中，我们研究了一种自然鲁棒的替代方式：信息传播的模式。在网上传播的未验证谣言的真实性是否仅根据其通过社交网络的扩散模式来辨别吗？ 使用图形内核从Twitter级联结构中提取复杂的拓扑信息，我们训练对语言，用户身份和时间视而不见的准确预测模型，这是第一次证明这种“消毒”的扩散模式对真实性非常有用。我们的结果表明，通过适当的汇总，即使在传播的早期阶段，人群的集体共享模式也可能揭示出有力的谣言或虚假的信号。
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.
