FALSE: Fake News Automatic and Lightweight Solution

TL;DR

This paper presents a lightweight, automated approach to fake news detection using R for data analysis, demonstrating high classifier efficiency in distinguishing real from fake news.

Contribution

It introduces a simple, effective fake news detection method utilizing R-based clustering and classification techniques, emphasizing lightweight implementation.

Findings

High classifier accuracy in fake news detection

Effective visualization of fake news data

Analysis demonstrates potential for lightweight solutions

Abstract

Fake news existed ever since there was news, from rumors to printed media then radio and television. Recently, the information age, with its communications and Internet breakthroughs, exacerbated the spread of fake news. Additionally, aside from e-Commerce, the current Internet economy is dependent on advertisements, views and clicks, which prompted many developers to bait the end users to click links or ads. Consequently, the wild spread of fake news through social media networks has impacted real world issues from elections to 5G adoption and the handling of the Covid- 19 pandemic. Efforts to detect and thwart fake news has been there since the advent of fake news, from fact checkers to artificial intelligence-based detectors. Solutions are still evolving as more sophisticated techniques are employed by fake news propagators. In this paper, R code have been used to study and visualize a modern fake news dataset. We use clustering, classification, correlation and various plots to analyze and present the data. The experiments show high efficiency of classifiers in telling apart real from fake news.