/fake-news-classifier

An end-to-end tool to efficiently classify fake news with 97% accuracy

Primary LanguageJupyter Notebook

Fake News Classification Tool

This project aims to build a fake news classifier that can accurately distinguish fake news from genuine news. We also developed a simple UI, to enable the users to efficiently verify news articles

Figure 1: Overview of the approach

Abstract:

  • The advent of technology and the development of social media platforms has made it easier to share news updates with the masses.
  • The effects of fake news can be catastrophic.
  • Fact checking can prevent people from reacting and taking action on fake news.
  • Extremely useful for news houses to fact-check their news before they share it with the masses.

Procedure:

  1. Dataset: 20,800 samples with 10387 real news,10413 fake news. Dataset is publically available here

  2. Preprocessing:

    1. Removing irrelevant texts and NAN values
    2. Stopwords
    3. Numerics and special characters
    4. Lemmatization
    5. Case folding
    6. Tokenization
    7. Padding

  3. Vectorisation:

    1. OneHot Encoding
    2. Count Vectoriser
    3. Hashing-Vectorizer
    4. TF-IDF
    5. GloVe Embedding
    6. Word2Vec Embedding
    7. BERT

  4. Machine Learning / Deep Learning Algorithms for Fake news classification:

    1. Naive Bayes (MultinomialNB)
    2. DecionTree
    3. AdaBoost Classification
    4. Logistic Regression
    5. Passive Aggressive
    6. Multilayer Perceptron
    7. LSTM
    8. BERT

Figure 2: Pipeline of the approach used

Pipeline & Output:

Output 1: BERT Tokeniser with Bert model

Figure 3: Pipeline for BERT Tokeniser with Bert model

Figure 4: Performance of BERT Tokeniser with Bert model

2: TF-IDF Tokenizer with Passive Aggressive Classifier

Figure 5: Pipeline for TF-IDF Tokenizer + Passive Aggressive Classifier

Figure 6: Performance of TF-IDF Tokenizer + Passive Aggressive Classifier

WebApp:

https://fake-news-detection-nlp.herokuapp.com/

  • An end-to-end deployed tool which allows user to verify news articles in a click
  • Efficient and accurate tool for fact checking
  • A simple minimalistic user interface
  • It allows user to input news text along with title and author name (both optional fields)
  • ‘Load sample input’ button allows users to understand and test the app

Technologies Used:

  1. Flask, HTML, CSS, JS for building the webapp
  2. Heroku for deploying the webapp
  3. Python, along with Machine learning, deep learning frameworks.

Team Members:

This project has been completed as a course project of CSE556: Natural Language Processing.

  1. Ria Gupta
  2. Ruhma Mehek Khan
  3. Prakriti Garg

Report & Slides

Presentation Slides

Project Report