Fake News Detection Engine
by kandikits Updated: Oct 20, 2022
AI fake news detector helps detect fake news through binary classification techniques and helps build better experiences by controlling the flow of disinformation in politics, businesses, climate change, and more. It's built on top of various powerful machine learning libraries. The tool works by training a neural network to spot fake articles based on their text content. When you run your own data through the tool, it gives you back a list of articles that it thinks are likely to be fake. You can then train the model further or decide if those results are acceptable or not. In addition to identifying fake news, this model can also be trained to identify real news. This allows you to compare the model's performance across different domains (e.g., politics vs. sports).
The following installer and deployment instructions will walk you through the steps of creating an AI fake news detector by using fakenews-detection, jupyter, vscode, and pandas. We will use fake news detection libraries (having fully modifiable source code) to customize and build a simple classifier that can detect fake news articles.
Fake News Predictor created using this kit are added in this section. The entire solution is available as a package to download from the source code repository.
For Windows OS,
- Download, extract and double-click the kit installer file to install the kit. Do ensure to extract the zip file before running it.
- To run, Double-click the extracted kit_installer file, in case if you get Windows Defender Smartscreen Warning, click More info and select Run anyway.
- The installation may take from 2 to 10 minutes based on bandwidth.
- When you're prompted during the installation of the kit, press Y to launch the app automatically and execute cells in the notebook by selecting Cell --> Run All from the Menu bar.
- To run the app manually, press N when you're prompted and locate the zip file kit_installer.zip.
- Extract the zip file and navigate to the directory fake-news-detection-main.
- Open a command prompt in the extracted directory of fake-news-detection and type the command jupyter notebook
- You will be directed to the fake-news-detection-main directory in jupyter notebook.
- Open FakeNewsdetection-starter.ipynb notebook.
- Execute cells in the notebook by selecting Cell --> Run All from Menu bar
For other Operating System,
- Click here to install python
- Click here to download the repository
- Extract the zip file and navigate to the directory fakenews-detection-main
- Open the terminal in the extracted directory fakenews-detection-main
- Install dependencies by executing the command pip install -r requirements.txt
- Run the command jupyter notebook for this kit e.g. Installation, Dependencies, etc.
Click on the button below to download the solution and follow the deployment instructions to begin set-up. This 1-click kit has all the required dependencies and resources you may need to build your Fake News Detection Engine.
For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community
VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web-based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.
Jupyter Interactive Notebook
Jupyter Notebook 9830 Version:v7.0.0a15 License: Permissive (BSD-3-Clause)
Exploratory Data Analysis
The fundamental package for scientific computing with Python.
Python 22957 Version:v1.24.2 License: Permissive (BSD-3-Clause)
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Python 37316 Version:v2.0.0rc1 License: Permissive (BSD-3-Clause)
Libraries in this group are used for the analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through a processing pipeline to become suitable for applying machine learning techniques and algorithms.
Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning.
scikit-learn: machine learning in Python
Python 53431 Version:1.2.2 License: Permissive (BSD-3-Clause)
The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.
matplotlib: plotting with Python
Python 17007 Version:v3.7.1 License: No License
Kit Solution Source
Fake News detection in news articles
Jupyter Notebook 0 Version:v1.0.0 License: Permissive (Apache-2.0)