Updated: Aug 22, 2023
1-Click Kit 
AI fake news detector helps detect fake news through binary classification methods. It helps build experiences by controlling the flow of disinformation. It's built on top of various powerful machine learning libraries. The tool works by training a Machine Learning model 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.
Deployment Information
This repository helps you build your own Fakenews detection application.
For Windows OS,
- Download, extract the zip file and run. Do ensure to extract the zip file before running it.
- After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook.
- To run the kit manually, press 'N' and follow the below steps. To run the solution anytime manually after installation, follow the below steps:
- Navigate to the 'speech-emotion-detection' folder located in C:\kandikits
- Open command prompt inside the extracted directory 'fakenews-detection'
- Run this command - "fakenews-detection-env\Scripts\activate.bat" to activate the virtual environment
- Run the command - "cd fakenews-detection"
- Run the command 'jupyter notebook' which would start a Jupyter notebook instance.
- Locate and open the 'FakeNewsdetection-starter.ipynb' notebook from the Jupyter Notebook browser window.
- Execute cells in the notebook.
For Linux distros and macOS,
- Follow the instructions to download & install Python3.9 & pip for your respective Linux distros or mac OS.
- Download the repository.
- Extract the zip file and navigate to the directory fakenews-detection.zip
- Open a terminal in the extracted directory 'fakenews-detection'
- Create and activate virtual environment using this command: 'virtualenv fakenews-venv & source ./fakenews-venv/bin/activate'
- Install dependencies using the command 'pip3.9 install -r requirements.txt'
- Once the dependencies are installed, run the command 'jupyter notebook' to start jupyter notebook (Pls use --allow-root if you're running as root)
- Locate and open the 'FakeNewsdetection-starter.ipynb' notebook from the Jupyter Notebook browser window.
- Execute cells in the notebook.
Click the button below to download the solution and follow the deployment information to begin set-up. This 1-click kit has all the required dependencies and resources to build your Fakenews Detection App.
Development Environment
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.
notebookby jupyter
10204 Version:v7.0.0b4Jupyter Interactive Notebook
notebookby jupyter
Jupyter Notebook 10204 Version:v7.0.0b4 License: Permissive (BSD-3-Clause)
Exploratory Data Analysis
numpyby numpy
23755 Version:v1.25.0rc1The fundamental package for scientific computing with Python.
numpyby numpy
Python 23755 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)
pandasby pandas-dev
38689 Version:v2.0.2Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pandasby pandas-dev
Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)
Text mining
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
Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning.
scikit-learnby scikit-learn
54584 Version:1.2.2scikit-learn: machine learning in Python
scikit-learnby scikit-learn
Python 54584 Version:1.2.2 License: Permissive (BSD-3-Clause)
Data Visualization
The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.
matplotlibby matplotlib
17559 Version:v3.7.1
License: No License (null)matplotlib: plotting with Python
matplotlibby matplotlib
Python 17559 Version:v3.7.1 License: No License
Kit Solution Source
fakenews-detectionby kandi1clickkits
0 Version:v1.0.0Fake News detection in news articles
fakenews-detectionby kandi1clickkits
Jupyter Notebook 0 Version:v1.0.0 License: Permissive (Apache-2.0)
Support
If you need help using this kit, you may reach us at the OpenWeaver Community.