fake news detector

Fake News Detector

Overview

Over the recent years, the growth of online social media has greatly facilitated the way people communicate with each other. The basic countermeasure of comparing websites against a list of labeled fake news sources is inflexible, and so a machine learning approach is desirable. Our project aims to use Natural Language Processing to detect fake news directly, based on the text content of news articles.

Problem Definition

Develop a machine learning program to identify when an article might be fake news. We aim to use a corpus of labeled real and fake news articles to build a classifier that can make decisions about information based on the content from the corpus. The model will focus on identifying fake news.

Dataset Description

train.csv: A full training dataset with the following attributes: id: unique id for a news article title: the title of a news article author: author of the news article text: the text of the article; could be incomplete label: a label that marks the article as potentially unreliable 1: unreliable 0: reliable

test.csv: A testing training dataset with all the same attributes at train.csv without the label.

Contents

• Text pre-processing techniques
• NLP Techniques ( TF-iDF, Bag-of-words)
• Logistic Regression
• Evaluation metrics and confusion matrix
• Model Deployment using Flask.

Try It Out

Clone the repo to your local machine-
> git clone https://github.com/sanikamal/fake-news-detector.git
> cd fake-news-detector

Make sure you have all the dependencies installed-

python 3.6+
numpy
pandas
matplotlib
sklearn
nltk

Comparing Accuracies of Models

Model	Accuracy
Logistic Regression	72.94%
MultinomialNB	88.42%

Fake News App

References

• Fake News Detection Project live session of utsav aggarwal
• Datasets from Kaggle