Fake News Detection via NLP is Vulnerable to Adversarial Attacks

TL;DR

This paper demonstrates that current NLP-based fake news detection methods are vulnerable to adversarial attacks and advocates for integrating fact-checking with linguistic analysis to improve accuracy.

Contribution

It highlights the vulnerability of existing fake news detectors to fact tampering attacks and proposes combining fact checking with linguistic features for better detection.

Findings

Fact tampering attacks can deceive state-of-the-art detectors

Current models often misclassify tampered fake news as real

Integrating fact checking can enhance detection robustness

Abstract

News plays a significant role in shaping people's beliefs and opinions. Fake news has always been a problem, which wasn't exposed to the mass public until the past election cycle for the 45th President of the United States. While quite a few detection methods have been proposed to combat fake news since 2015, they focus mainly on linguistic aspects of an article without any fact checking. In this paper, we argue that these models have the potential to misclassify fact-tampering fake news as well as under-written real news. Through experiments on Fakebox, a state-of-the-art fake news detector, we show that fact tampering attacks can be effective. To address these weaknesses, we argue that fact checking should be adopted in conjunction with linguistic characteristics analysis, so as to truly separate fake news from real news. A crowdsourced knowledge graph is proposed as a straw man solution to collecting timely facts about news events.