Fake News Detection via NLP is Vulnerable to Adversarial Attacks
This addresses a critical vulnerability in fake news detection systems, which is important for public trust and information integrity, though it is incremental as it builds on existing methods.
The paper demonstrates that existing NLP-based fake news detectors, like Fakebox, are vulnerable to fact-tampering attacks, potentially misclassifying fake and real news, and proposes integrating fact-checking with linguistic analysis to improve detection.
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.