Fully Automated Fact Checking Using External Sources

TL;DR

This paper presents a fully automated fact-checking framework that leverages external web sources and deep neural networks to verify claims, demonstrating effectiveness in rumor detection and community question answering.

Contribution

It introduces a novel deep learning-based framework that uses external web sources and task-specific embeddings for automatic fact checking.

Findings

Good performance on rumor detection

Effective in community question answering

Utilizes web sources for verification

Abstract

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.