Fully Automated Fact Checking Using External Sources
This addresses the challenge of combating false claims online, though it appears incremental as it builds on existing neural network and external source methods.
The paper tackles the problem of automatically verifying claims by proposing a fully-automated framework that uses the entire Web as an external knowledge source, achieving good performance on rumor detection and fact checking in community question answering forums.
Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.