Fake News Detection Using LLMs

📌 Description

This repository contains the implementation of the paper “Toward Fair and Effective Fake News Detection: Assessing Large Language Models.” The project focuses on evaluating the fairness and efficiency of Large Language Models (LLMs) in detecting fake news using a dataset of news articles classified by political leaning.

🚀 Features

• Analysis of LLM biases in news classification
• Evaluation of model fairness and accuracy
• Benchmarking multiple LLMs (GPT-4o, LLaMa, Qwen, Deepseek)
• News leaning classification (Democrat, Republican, Neutral, Varies)
• Fake news detection using a labeled dataset

📖 Usage

use the news_dataset.csv and news_leaning_dataset.csv as dataset and log_processor.py as processor for the outputs of each LLM.

🛠️ Technologies Used

• Python
• open-ai
• Scikit-learn
• Pandas & NumPy

📊 Dataset

The dataset consists of news articles labeled with political leanings and fact-checking results. The files include:

• news_dataset.csv: Contains raw news articles with metadata with labeled POV.
• news_leaning_dataset.csv: Labels news articles as Democrat, Republican, Neutral, or Varies with Labeled leanings.