A Proposed Bi-LSTM Method to Fake News Detection

TL;DR

This paper introduces a Bi-LSTM based approach for fake news detection, demonstrating its effectiveness with an 84% accuracy and 62.0 F1-macro score on collected data from various sources.

Contribution

The study applies Bidirectional LSTM to fake news detection, showcasing its potential as a tool for identifying false information on social media.

Findings

Achieved 84% accuracy in fake news classification

Obtained 62.0 F1-macro score on test data

Validated Bi-LSTM effectiveness for fake news detection

Abstract

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.