SGG: Spinbot, Grammarly and GloVe based Fake News Detection

TL;DR

This paper introduces a fake news detection system that combines Spinbot, Grammarly, and GloVe to extract features, achieving state-of-the-art results and demonstrating robustness across different datasets and domains.

Contribution

The paper presents a novel approach that leverages paraphrasing, grammar-checking, and word-embedding tools for effective fake news detection, showing improved robustness and accuracy.

Findings

Achieved state-of-the-art results on Fake News AMT dataset.

Demonstrated comparable performance on Celebrity datasets.

Proved robustness through cross-domain and multi-domain analysis.

Abstract

Recently, news consumption using online news portals has increased exponentially due to several reasons, such as low cost and easy accessibility. However, such online platforms inadvertently also become the cause of spreading false information across the web. They are being misused quite frequently as a medium to disseminate misinformation and hoaxes. Such malpractices call for a robust automatic fake news detection system that can keep us at bay from such misinformation and hoaxes. We propose a robust yet simple fake news detection system, leveraging the tools for paraphrasing, grammar-checking, and word-embedding. In this paper, we try to the potential of these tools in jointly unearthing the authenticity of a news article. Notably, we leverage Spinbot (for paraphrasing), Grammarly (for grammar-checking), and GloVe (for word-embedding) tools for this purpose. Using these tools, we were able to extract novel features that could yield state-of-the-art results on the Fake News AMT dataset and comparable results on Celebrity datasets when combined with some of the essential features. More importantly, the proposed method is found to be more robust empirically than the existing ones, as revealed in our cross-domain analysis and multi-domain analysis.