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,
For other Operating System,
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.
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.
Python 36688 Version:1.5.2 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.
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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