# A Proposed Bi-LSTM Method to Fake News Detection

arXiv: https://arxiv.org/abs/2206.13982 · HTML: https://mlsanity.com/paper/2206.13982/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19](https://mlsanity.com/paper/2006.11343/) (2020)
- [Evaluating Deep Learning Approaches for Covid19 Fake News Detection](https://mlsanity.com/paper/2101.04012/) (2021)
- [Detecting Fake News with Capsule Neural Networks](https://mlsanity.com/paper/2002.01030/) (2020)
- [Transforming Fake News: Robust Generalisable News Classification Using Transformers](https://mlsanity.com/paper/2109.09796/) (2021)
- [AMUSED: An Annotation Framework of Multi-modal Social Media Data](https://mlsanity.com/paper/2010.00502/) (2020)

## Built on by (most cited first, 2 of 2)

- [Enhancing Bangla Fake News Detection Using Bidirectional Gated Recurrent Units and Deep Learning Techniques](https://mlsanity.com/paper/2404.01345/) (2024)
- [Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI](https://mlsanity.com/paper/2404.04686/) (2024)

## Abstract (arXiv)

Recent years have seen an explosion in social media usage, allowing people to connect with others. Since the appearance of platforms such as Facebook and Twitter, such platforms influence how we speak, think, and behave. This problem negatively undermines confidence in content because of the existence of fake news. For instance, false news was a determining factor in influencing the outcome of the U.S. presidential election and other sites. Because this information is so harmful, it is essential to make sure we have the necessary tools to detect and resist it. We applied Bidirectional Long Short-Term Memory (Bi-LSTM) to determine if the news is false or real in order to showcase this study. A number of foreign websites and newspapers were used for data collection. After creating & running the model, the work achieved 84% model accuracy and 62.0 F1-macro scores with training data.