Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2025
|
| Online Access: | https://doi.org/10.5281/zenodo.16793593 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- <p>The Internet's rapid development makes it possible for information to spread quickly through websites<br>or social networks. Fake or unverified news spreads on social media and reaches thousands of users<br>without anyone questioning its veracity. Misinformation or fabricated news that spreads on social media<br>with the intention of harming a person, group, or agency is known as fake news. Various machine-learning<br>techniques have been used to distinguish fake news from real. In this paper, we tried to achieve this using<br>machine learning techniques and natural language processing. We use frequency-inverse document<br>frequency as a feature extraction method and logistic regression, support vector machine and Naive Bayes<br>as the classifier. We use a dataset with labels for fake and real news to train our model. </p>