The spread of misinformation poses a threat to the social media ecosystem. Effective countermeasures to mitigate this threat require that social media platforms be able to accurately detect low-credibility accounts even before the content they share can be classified as misinformation. Here we present methods to infer account credibility from information diffusion patterns, in particular leveraging two networks: the reshare network, capturing an account’s trust in other accounts, and the bipartite account-source network, capturing an account’s trust in media sources. We extend network centrality measures and graph embedding techniques, systematically comparing these algorithms on data from diverse contexts and social media platforms. We demonstrate that both kinds of trust networks provide useful signals for estimating account credibility. Some of the proposed methods yield high accuracy, providing promising solutions to promote the dissemination of reliable information in online communities. Two kinds of homophily emerge from our results: accounts tend to have similar credibility if they reshare each other’s content or share content from similar sources. Our methodology invites further investigation into the relationship between accounts and news sources to better characterize misinformation spreaders.

错误信息的传播对社交媒体生态系统构成威胁。减轻这种威胁的有效对策要求社交媒体平台能够准确检测低可信度帐户，甚至在它们共享的内容被归类为错误信息之前。在这里，我们提出了从信息传播模式推断帐户可信度的方法，特别是利用两个网络：转发网络，捕获帐户对其他帐户的信任，以及双向帐户源网络，捕获帐户对媒体源的信任。我们扩展了网络中心性度量和图嵌入技术，系统地比较这些算法对来自不同上下文和社交媒体平台的数据。我们证明这两种信任网络都为估计帐户可信度提供了有用的信号。所提出的一些方法具有很高的准确性，为促进在线社区中可靠信息的传播提供了有前景的解决方案。我们的结果出现了两种同质性：如果帐户转发彼此的内容或共享来自相似来源的内容，则往往具有相似的可信度。我们的方法要求进一步调查账户和新闻来源之间的关系，以更好地描述错误信息传播者的特征。