A Comprehensive Review of Traditional Methods for Fake News Detection

Abstract

The spread of fake news presents significant challenges to maintaining information integrity, particularly on digital platforms where misinformation can quickly go viral. While machine learning has emerged as a key tool for detecting fake news, traditional and manual methods remain crucial due to their accessibility, transparency, and ability to be applied in real time. This paper presents a comprehensive review of non-machine learning methods for detecting fake news, drawing insights from 25 significant studies. The methods discussed include content analysis, linguistic cues for detecting deception, manual fact-checking, crowdsourced evaluations, and media literacy initiatives. While each approach has distinct advantages, they also face limitations such as slow processing, scalability issues, and potential bias. Fact-checking can be time-consuming, media literacy requires sustained educational efforts, and crowdsourcing may lack consistency. This review also explores opportunities to enhance these traditional methods, including the potential for hybrid systems that integrate manual approaches with newer technologies to improve their effectiveness in addressing fake news.

Introduction: The rise of digital platforms has fuelled the spread of fake news, posing challenges to journalism, politics, and public health. While machine learning offers fast and accurate fake news detection, it requires resources and lacks transparency. Traditional methods like content analysis, manual fact-checking, crowdsourcing, and media literacy remain essential for combating misinformation. Combining both machine learning and traditional approaches creates a more robust strategy for addressing the growing problem of fake news.

Literature Review: The literature review examines traditional fake news detection methods like content analysis, fact-checking, crowdsourcing, and media literacy. These approaches analyze linguistic cues, verify claims with trusted sources, and educate individuals on assessing information. Though time-consuming, prone to bias, and difficult to scale, they remain vital in contexts where machine learning is impractical, offering transparency and human oversight to combat misinformation and preserve media integrity.

Methodology: This paper conducts a systematic review of 25 academic studies on traditional fake news detection methods, including content analysis, manual fact-checking, crowdsourcing, and media literacy. By analysing the methodologies, results, and limitations of each approach, the review compares their strengths and weaknesses. It identifies research gaps and suggests areas for improvement, synthesizing findings into a framework for understanding the effectiveness of non-machine learning techniques in combating misinformation.

Conclusions: Combating fake news requires a multifaceted strategy combining manual fact-checking, crowdsourcing, and media literacy. Fact-checking ensures accuracy but lacks scalability, while crowdsourcing offers quick detection, though it risks bias. Media literacy empowers individuals to evaluate information critically, though its reach is limited. Integrating machine learning improves detection speed and efficiency. A combined approach strengthens efforts to fight misinformation, fostering a more informed and resilient public.