AI-Driven Fake News Detection: Trends, Techniques, and Experimental Analysis

Abstract

The rapid spread of fake news poses a significant challenge to information accuracy. This thesis highlights fake news definitions and characteristics, introducing a taxonomy that categorizes AI-driven detection methods into model-centric and process-centric approaches. We evaluate various approaches ranging from traditional machine learning to trending AI methodologies, focusing on techniques like data augmentation, information extraction, and results explanation.

To re-evaluate classical algorithms, this work provides a detailed analysis of a 2016 U.S. election dataset. By employing fact-checking and advanced data mining, we investigate linguistic characteristics through exploratory data analysis and apply multiple machine learning algorithms for classification.

Experimental results yield valuable insights into the defining characteristics of fake news and demonstrate machine learning's potential to enhance misinformation filtering. Finally, we discuss four main challenges and trends aimed at refining detection accuracy and integrating cutting-edge AI methodologies to combat fake news more effectively.