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Covid Fake News Analysis

Table of Content

Results

Method Used Result
CLASSIFICATION REPORT Results

Synopsis

COVID Fake News is used to classify the real news from fake news, this data consists of all the real and fake news about Covid 19 that is spread on Twitter, and in this project, I have built a classification model to find the real news from those fake news and vice versa. Here, I have used various models, and the best is considered to proceed with the classification of the news. The data consists of the tweeted news and the label specifying whether the news is fake or real.

So based on the given data,

Independent variable (X): Tweet news

Dependent variable (Y): Label value

Based on the given data we can identify this as a classification problem, so we can use various machine learning problems to solve this problem which are as follows:

Logistic regression
Naive Bayes
Passive Aggressive Algorithm
Support Vector Classifier

Based on the accuracy considered, Support Vector Classifier is considered to proceed with the classification.

Machine learning models : Support Vector Classifier

Data preprocessing : Pandas, NLTK

Data visualization : Matplotlib

Appendix

The requirement for developing this model is present in requirements.txt file.

The development of the model is present in main.ipynb file.

Links

Directory Tree

├── Classification_report.png
├── Covid Fake News Analysis.ipynb
├── Data.xlsx
├── Fake news Word cloud.jpg
├── LICENSE
├── README.md
├── Real news Word cloud.jpg
├── The most fake news repeated.png
├── The most real news repeated.png
├── requirements.txt

Features

  • Real-time monitoring of Twitter feeds for COVID-19-related news.
  • Adjustable threshold for classifying news as fake or real.
  • Reporting and tracking system for identified fake news.

Run Locally

Clone the project

  git clone https://github.com/Vedakeerthi/Covid-Fake-news-analysis.git

Install dependencies

  pip install -r requirements.txt

Start the server and run the file

  python Covid Fake News Analysis.ipynb

License

GPL-3.0 License

Technology Used

python   scikit_learn   matplotlib