AI fake news detector helps detect fake news through binary classification methods. It builds better experiences by controlling the disinformation flow.
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. kandi kit provides you with a fully deployable AI Fake News Detector. Source code included so that you can customize it for your requirement. With this kit, you can 1. Use a pre-trained model for detecting fake news. 2. Train the model on your custom dataset. 3. Expose the fake news detection as an API Add-on on examples are also included as given below 1. Use web scraper to automatically make your training dataset. 2. Visualise training and prediction data for useful insights.
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.
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Deployment Information
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. Note: Do ensure to extract the zip file before running it. The installation may take from 2 to 10 minutes based on bandwidth.
1. 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 Menu bar
2. To run the app manually, press N when you're prompted and locate the zip file 'kit_installer.zip'
3. Extract the zip file and navigate to the directory 'fake-news-detection-main'
4. Open command prompt in the extracted directory 'fake-news-detection' and run the command 'jupyter notebook'
For other Operating System,
2. download the repository
3. Extract the zip file and navigate to the directory 'fakenews-detection-main'
4. Open terminal in the extracted directory 'fakenews-detection-main'
5. Install dependencies by executing the command 'pip install -r requirements.txt'
6. Run the command ‘jupyter notebook’ and select the notebook ‘FakeNewsdetection-starter.ipynb’ on the browser window.
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 3. Once all the cells of the notebook are executed, the prediction result will be written to the file 'fake_news_test_output.csv' Training with your dataset: 1. Add news articles to a csv file under a column name 'news_text'. 2. Add corresponding labels as 'real' or 'fake' denoting whether a news article is real or not. 3. You can refer to the file 'fake_news_train.csv' for an example. 4. Set the variable for training file in the notebook under Variables section. Testing with your dataset: 1. Add news articles to a csv file under a column name 'news_text'. 2. You can refer to the file 'fake_news_test.csv' for an example. 3. Set the variable for testing file in the notebook under Variables section. You can execute the cells of notebook by selecting Cell from the menu bar. For any support, you can reach us at FAQ & Support
Kit Solution Source
fakenews-detectionby kandikits
Fake News detection in news articles
fakenews-detectionby kandikits
Jupyter Notebook 0 Version:v1.0.0 License: Permissive (Apache-2.0)
Libraries useful for this solution
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.
notebookby jupyter
Jupyter Interactive Notebook
notebookby jupyter
Jupyter Notebook 10204 Version:v7.0.0b4 License: Permissive (BSD-3-Clause)
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.
numpyby numpy
The fundamental package for scientific computing with Python.
numpyby numpy
Python 23755 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)
pandasby pandas-dev
Flexible 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 analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms.
spaCyby explosion
💫 Industrial-strength Natural Language Processing (NLP) in Python
spaCyby explosion
Python 26383 Version:v3.2.6 License: Permissive (MIT)
py-lingualyticsby lingualytics
A text analytics library with support for codemixed data
py-lingualyticsby lingualytics
Python 32 Version:Current License: Permissive (MIT)
Machine Learning
Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning.
scikit-learnby scikit-learn
scikit-learn: machine learning in Python
scikit-learnby scikit-learn
Python 54584 Version:1.2.2 License: Permissive (BSD-3-Clause)
faissby facebookresearch
A library for efficient similarity search and clustering of dense vectors.
faissby facebookresearch
C++ 22571 Version:v1.7.4 License: Permissive (MIT)
sentence-transformersby UKPLab
Multilingual Sentence & Image Embeddings with BERT
sentence-transformersby UKPLab
Python 10938 Version:v2.2.2 License: Permissive (Apache-2.0)
Data Visualization
The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.
seabornby mwaskom
Statistical data visualization in Python
seabornby mwaskom
Python 10797 Version:v0.12.2 License: Permissive (BSD-3-Clause)
matplotlibby matplotlib
matplotlib: plotting with Python
matplotlibby matplotlib
Python 17559 Version:v3.7.1 License: No License
Troubleshooting
1. If you encounter any error related to MS Visual C++, please install MS Visual Build tools 2. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway. 3. During kit installer, if you encounter Windows security alert, click Allow. 4. If you encounter Memory Error, check if the available memory is sufficient and it is proportion to the size of the data being used. For our dataset, the minimum required memory is 8GB. If your computer doesn't support standard commands from windows 10, you can follow the instructions below to finish the kit installation. 1. Click here to install python 2. Click here to download the repository 3. Extract the zip file and navigate to the directory 'fakenews-detection-main' 4. Open terminal in the extracted directory 'fakenews-detection-main' 5. Install dependencies by executing the command 'pip install -r requirements.txt' 6. Run the command ‘jupyter notebook’ and select the notebook ‘FakeNewsdetection-starter.ipynb’ on the browser window.
Support
For any support, you can reach us at FAQ & Support