Build Fake News Detection Engine in AI

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by kandikits dot icon Updated: Aug 22, 2023

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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,

  1. Download, extract the zip file and run. Do ensure to extract the zip file before running it.
  2. After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook.
  3. To run the kit manually, press 'N' and follow the below steps. To run the solution anytime manually after installation, follow the below steps:
  4. Navigate to the 'speech-emotion-detection' folder located in C:\kandikits
  5. Open command prompt inside the extracted directory 'fakenews-detection'
  6. Run this command - "fakenews-detection-env\Scripts\activate.bat" to activate the virtual environment
  7. Run the command - "cd fakenews-detection"
  8. Run the command 'jupyter notebook' which would start a Jupyter notebook instance.
  9. Locate and open the 'FakeNewsdetection-starter.ipynb' notebook from the Jupyter Notebook browser window.
  10. Execute cells in the notebook.


For Linux distros and macOS,

  1. Follow the instructions to download & install Python3.9 & pip for your respective Linux distros or mac OS.
  2. Download the repository.
  3. Extract the zip file and navigate to the directory fakenews-detection.zip
  4. Open a terminal in the extracted directory 'fakenews-detection'
  5. Create and activate virtual environment using this command: 'virtualenv fakenews-venv & source ./fakenews-venv/bin/activate'
  6. Install dependencies using the command 'pip3.9 install -r requirements.txt'
  7. Once the dependencies are installed, run the command 'jupyter notebook' to start jupyter notebook (Pls use --allow-root if you're running as root)
  8. Locate and open the 'FakeNewsdetection-starter.ipynb' notebook from the Jupyter Notebook browser window.
  9. 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.

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Visual Studio Code

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                      notebookby jupyter

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                      Jupyter Interactive Notebook

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                                notebookby jupyter

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                                          Exploratory Data Analysis

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                                          The fundamental package for scientific computing with Python.

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                                                              Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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                                                                        pandasby pandas-dev

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                                                                                  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.

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                                                                                                      Machine Learning

                                                                                                      Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning.

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                                                                                                      scikit-learn: machine learning in Python

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                                                                                                                          Data Visualization

                                                                                                                          The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.

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                                                                                                                                              fakenews-detectionby kandi1clickkits

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                                                                                                                                              Fake News detection in news articles

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                                                                                                                                                        fakenews-detectionby kandi1clickkits

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                                                                                                                                                        Fake News detection in news articles
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                                                                                                                                                                  If you need help using this kit, you may reach us at the OpenWeaver Community.

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