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Build AI Fake News Detector

by kandikits Updated: Jan 6, 2023

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. 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.

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,


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.

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

Jupyter Notebook star image 0 Version:v1.0.0

License: Permissive (Apache-2.0)

Fake News detection in news articles

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fakenews-detectionby kandikits

Jupyter Notebook star image 0 Version:v1.0.0 License: Permissive (Apache-2.0)

Fake News detection in news articles
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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.

vscodeby microsoft

TypeScript star image 141808 Version:1.74.3

License: Permissive (MIT)

Visual Studio Code

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vscodeby microsoft

TypeScript star image 141808 Version:1.74.3 License: Permissive (MIT)

Visual Studio Code
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notebookby jupyter

Jupyter Notebook star image 9702 Version:v7.0.0a11

License: Others (Non-SPDX)

Jupyter Interactive Notebook

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notebookby jupyter

Jupyter Notebook star image 9702 Version:v7.0.0a11 License: Others (Non-SPDX)

Jupyter Interactive Notebook
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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

Python star image 22526 Version:1.24.1

License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

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numpyby numpy

Python star image 22526 Version:1.24.1 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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pandasby pandas-dev

Python star image 36688 Version:1.5.2

License: Permissive (BSD-3-Clause)

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

Python star image 36688 Version:1.5.2 License: Permissive (BSD-3-Clause)

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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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.

nltkby nltk

Python star image 11448 Version:3.8.1

License: Permissive (Apache-2.0)

NLTK Source

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nltkby nltk

Python star image 11448 Version:3.8.1 License: Permissive (Apache-2.0)

NLTK Source
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spaCyby explosion

Python star image 25086 Version:3.4.4

License: Permissive (MIT)

💫 Industrial-strength Natural Language Processing (NLP) in Python

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spaCyby explosion

Python star image 25086 Version:3.4.4 License: Permissive (MIT)

💫 Industrial-strength Natural Language Processing (NLP) in Python
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py-lingualyticsby lingualytics

Python star image 32 Version:Current

License: Permissive (MIT)

A text analytics library with support for codemixed data

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py-lingualyticsby lingualytics

Python star image 32 Version:Current License: Permissive (MIT)

A text analytics library with support for codemixed data
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Machine Learning

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

scikit-learnby scikit-learn

Python star image 52698 Version:1.2.0

License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python

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scikit-learnby scikit-learn

Python star image 52698 Version:1.2.0 License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python
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faissby facebookresearch

C++ star image 19007 Version:1.5.3

License: Permissive (MIT)

A library for efficient similarity search and clustering of dense vectors.

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faissby facebookresearch

C++ star image 19007 Version:1.5.3 License: Permissive (MIT)

A library for efficient similarity search and clustering of dense vectors.
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sentence-transformersby UKPLab

Python star image 9237 Version:2.2.2

License: Permissive (Apache-2.0)

Multilingual Sentence & Image Embeddings with BERT

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sentence-transformersby UKPLab

Python star image 9237 Version:2.2.2 License: Permissive (Apache-2.0)

Multilingual Sentence & Image Embeddings with BERT
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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.

seabornby mwaskom

Python star image 10246 Version:0.12.2

License: Permissive (BSD-3-Clause)

Statistical data visualization in Python

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seabornby mwaskom

Python star image 10246 Version:0.12.2 License: Permissive (BSD-3-Clause)

Statistical data visualization in Python
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matplotlibby matplotlib

Python star image 16767 Version:3.6.2

License: No License (null)

matplotlib: plotting with Python

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matplotlibby matplotlib

Python star image 16767 Version:3.6.2 License: No License

matplotlib: plotting with Python
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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

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