Machine Learning Techniques for Analyzing Fake News

Abstract

Online media, encompassing various social media platforms, has revolutionized communication and information exchange globally. It connects individuals across vast distances, enabling real-time data sharing and fostering cultural awareness. Despite its benefits, the pervasive nature of online media has also facilitated the spread of fake news, causing misinformation to increase rapidly. This phenomenon has significant repercussions, including public unrest and social division.

Fake news, defined as intentionally misleading or false information, challenges discerning factual content. Various detection methods are explored to address this, including developing algorithms and machine learning models. Techniques such as Natural Language Processing (NLP) and web scraping are employed to curate and analyze data, helping to verify the authenticity of news sources and content.

This paper reviews the existing literature on fake news detection and proposes a methodological framework leveraging machine learning. The proposed system evaluates news authenticity by examining sources, timing, and information location. By assigning credibility scores to news articles, the system aims to mitigate the spread of misinformation on social media platforms. This approach underscores the importance of integrating Artificial Intelligence (AI) in combating the detrimental effects of fake news in the digital age.