Fake-news-Detection-with-deep-learning
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开发工具：Jupyter Notebook
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上传日期：2023-08-12 10:36:04
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sh-1993
说明： 该项目将通过查找新闻网站的关键字（如“POPE”、“TRUMP”等）来检测假新闻文章（使用suff...，
(This project will seek to detect fake news articles by looking out for keywords (such as "POPE", "TRUMP" etc.) from news sites (with sufficiently large datasets) and then comparing them to a dataset (from Kaggle). The model has been trained to report an avg accuracy of 90%.)
文件列表:
code.ipynb (19679, 2023-08-12)
# Fake News Detection with Deep Learning
In the digital age, the rapid dissemination of information has led to a surge in the spread of fake news. This repository is dedicated to addressing this challenge by using deep learning techniques to detect and filter out fake news articles.
## Overview
- **Objective**: To create a reliable and efficient model that can discern between genuine and fabricated news articles.
- **Technique**: The project employs deep learning, specifically neural networks, to understand the intricacies and patterns of fake news.
## Dataset
The backbone of any machine learning project is its dataset. This project utilizes:
- A comprehensive dataset containing a mix of genuine and fake news articles.
- Data preprocessing techniques to clean and structure the data for optimal training.
## Model Architecture
- The neural network is designed to capture the nuances of natural language, making it adept at detecting inconsistencies or patterns typical of fake news.
- Regularization techniques are employed to prevent overfitting, ensuring the model generalizes well to new, unseen data.
## Applications
This deep learning model is not just a proof of concept but has real-world applications:
- **News Aggregators**: Integrate the model to ensure that the news being aggregated from various sources is genuine.
- **Social Media Platforms**: With the rise of shared news articles on platforms like Facebook and Twitter, this model can be a crucial tool in verifying the authenticity of articles before they're spread.
- **Educational Tools**: Can be used as a tool to educate individuals on discerning between real and fake news.
## Getting Started
For those interested in exploring, using, or contributing to this project:
1. Clone the repository and set up the required environment.
2. Follow the detailed instructions provided in the repository to train or test the model.
3. Contributions, whether in the form of feedback, bug reports, or code, are always welcome!
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By leveraging the power of deep learning, this project aims to make a tangible difference in the fight against fake news. Your support and contributions can help in refining and enhancing its capabilities.
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