Weighted Accuracy Algorithmic Approach In Counteracting Fake News And Disinformation

TL;DR

This paper introduces a methodology that uses a weighted accuracy approach combining four machine learning algorithms to detect and report fake news and disinformation effectively.

Contribution

It presents a novel constraint mechanism leveraging combined weighted accuracies of multiple algorithms for improved fake news detection.

Findings

Enhanced detection accuracy with the combined approach

Effective identification of fake news and disinformation

Potential for real-time reporting and mitigation

Abstract

As the world is becoming more dependent on the internet for information exchange, some overzealous journalists, hackers, bloggers, individuals and organizations tend to abuse the gift of free information environment by polluting it with fake news, disinformation and pretentious content for their own agenda. Hence, there is the need to address the issue of fake news and disinformation with utmost seriousness. This paper proposes a methodology for fake news detection and reporting through a constraint mechanism that utilizes the combined weighted accuracies of four machine learning algorithms.