# SGG: Spinbot, Grammarly and GloVe based Fake News Detection

arXiv: https://arxiv.org/abs/2008.06854 · HTML: https://mlsanity.com/paper/2008.06854/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [Weakly Supervised Veracity Classification with LLM-Predicted Credibility Signals](https://mlsanity.com/paper/2309.07601/) (2023)
- [Paraphrase Identification with Deep Learning: A Review of Datasets and Methods](https://mlsanity.com/paper/2212.06933/) (2022)
- [Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions](https://mlsanity.com/paper/2201.00768/) (2022)
- [Introducing Rhetorical Parallelism Detection: A New Task with Datasets, Metrics, and Baselines](https://mlsanity.com/paper/2312.00100/) (2023)
- [Deception Detection with Feature-Augmentation by soft Domain Transfer](https://mlsanity.com/paper/2305.01011/) (2023)
- [An exploration of features to improve the generalisability of fake news detection models](https://mlsanity.com/paper/2502.20299/) (2025)
- [A Systematic Review on the Detection of Fake News Articles](https://mlsanity.com/paper/2110.11240/) (2021)
- [Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?](https://mlsanity.com/paper/2111.05139/) (2021)

## Abstract (arXiv)

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.