Fake News Detection

This project uses machine learning models to classify news articles as real or fake based on text structure and content features.

What We Did

• Extracted features like article length, vocabulary richness, and keyword frequencies.
• Applied Principal Component Analysis (PCA) to reduce dimensionality.
• Trained and compared two models:
  • Random Forest (using ranger)
  • Support Vector Machine (SVM) both with linear and radial kernel

Results

• Both models achieved over 83% accuracy and about 70% Kappa .
• SVM performed slightly better at detecting fake news.
• Random Forest offered better interpretability through variable importance.

Files

• *.Rmd: Main analysis and model building
• news_data.csv: Datasets used
• README.md: Project summary

Motivation

In an age of misinformation, building tools to automatically identify fake news is critical for protecting the public and supporting media literacy.

Requirements

• R
• Libraries: library(tidyverse) library(dplyr) library(tidytext) library(SnowballC) library(tm) library(tidytext) library(e1071) library(caret) library(ranger) library(ggplot2)