Summary of A Regularized Lstm Method For Detecting Fake News Articles, by Tanjina Sultana Camelia et al.
A Regularized LSTM Method for Detecting Fake News Articles
by Tanjina Sultana Camelia, Faizur Rahman Fahim, Md. Musfique Anwar
First submitted to arxiv on: 16 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an advanced machine learning solution to detect fake news articles. The approach leverages a comprehensive dataset containing 23,502 fake news articles and 21,417 accurate news articles. Three machine-learning models are implemented and evaluated, including Long Short-Term Memory (LSTM) networks with regularization techniques and hyperparameter tuning. The models achieve high accuracy rates, peaking at 98%, demonstrating the effectiveness of this approach in identifying fake news. The work showcases advancements in natural language processing and machine learning, contributing valuable tools for combating misinformation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fight fake news by using special computer programs to tell real from fake news articles. The researchers made a big dataset with lots of news articles, both true and false. They tested three types of computer models to see which one worked best at spotting fake news. The best model was super accurate, getting it right almost all the time! This means we can use these special programs to help keep our news truthful and trustworthy. |
Keywords
» Artificial intelligence » Hyperparameter » Lstm » Machine learning » Natural language processing » Regularization
