Fake News Detector- Reehana Kit

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. You can add an input file with your own customized data in CSV file format. The corresponding output CSV file will be generated in your fakenews-detection-main directory. kandi kit provides you with a fully deployable AI Fake News Detector. Source code included so that you can customize it for your requirement. ⬇️ Download 1-Click Installer

Training and Certification - How to build fake news detection using machine learning

Watch this self-guided tutorial on how you can use training data, NLP pipeline, TF-IDF vectorizer, and text classifier to build your own AI fake new detector. Completed the training? Apply for your Participation Certificate and Achievement Certificate now! Apply for Certification Tag us on social media with a screenshot or video of your working application for a chance to be featured as an Open Source Champion and get a verified badge.

Instructions to Run

Follow the below instructions to run the solution. 1. Locate and open the FakeNewsDetection-starter.ipynb notebook from the Jupyter Notebook browser window. 2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar For configuring with your data, 1. Place your csv data file with columns as 'text' and 'label' containing the 'text data' and 'predicted label' respectively as in the sample 'fakenews.csv' in the fakenews-detection-master directory from the kit_installer.bat location. 2. Replace the filename in the 'Variables' section of the notebook to your csv file name. 3. Execute cells in the notebook by selecting Cell --> Run All from Menu bar. Input file: fakenews.csv - contains sample data for Training and Predicting fake news. It has 2 columns: 'text' and 'label'. Attributes of fakenews.csv dataset: 1.text: text of the article 2.label: a label that marks the article as potentially unreliable with 2 values '1' and '0' 1: fake 0: true You can additionally try developing more ML models and include more enhancements for additional scores.

For Your Reference

After executing jupyter notebook, you will get the Evaluation report for Training data and an output CSV file for 'fakenewstest.csv'. The following snapshots represent the Metrics of Classification Report and Confusion Matrix for 80% of training data and 20% of test data respectively. Metrics of Classification Report for Training data Confusion Matrix for Training data Metrics of Classification Report for Test data Confusion Matrix for Training data Below is a snapshot of an output CSV file generated for the input test file 'fakenewstest.csv' :

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. Jupyter Notebook is used for our development.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

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.

Data Visualization

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

Troubleshooting

1. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway 2. During kit installer, if you encounter Windows security alert, click Allow

Support

For any support, you can direct message us at #help-with-kandi-kits