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The volume of information generated online makes it impossible to manually fact-check all claims. Computational approaches for fact checking may be the key to help mitigate the risks of massive misinformation spread. Such approaches can be designed to not only be scalable and effective at assessing veracity of dubious claims, but also to boost a human fact checker's productivity by surfacing relevant facts and patterns to aid their analysis. We present a novel, unsupervised network-flow based approach to determine the truthfulness of a statement of fact expressed in the form of a triple. We view a knowledge graph of background information about real-world entities as a flow network, and show that computational fact checking then amounts to finding a &#x0022;knowledge stream&#x0022; connecting the subject and object of the triple. Evaluation on a range of real-world and hand-crafted datasets of facts reveals that this network-flow model can be very effective in discerning true statements from false ones, outperforming existing algorithms on many test cases. Moreover, the model is expressive in its ability to automatically discover several useful patterns and surface relevant facts that may help a human fact checker.

Finding Streams in Knowledge Graphs to Support Fact Checking