LINGUISTIC ATTRIBUTES AS A VALID TOOL FOR IDENTIFYING FAKE NEWS IN SOCIAL MEDIA PLATFORMS

Abstract

The mass-spread of fake news on social media platforms has emerged as a significant challenge for many online users in recent years. This issue has prompted researchers to develop various mechanisms aimed at detecting and preventing the spread of misinformation. While numerous techniques have been proposed to identify fake content, many are hindered by inherent limitations and inaccuracies. The primary objective of this research paper is to analyze the linguistic attributes of news articles and identify key differences that distinguish fake news from authentic news. Utilizing a qualitative data analysis approach, the following study makes an attempt to examine the systematic linguistic features of both fake and legitimate news articles. The dataset comprises 20 news articles from American news websites and 20 fake news articles sourced from the Facebook social networking website. The findings clearly demonstrate that linguistic features can serve as effective indicators to help online users differentiate between unreliable and factual news content. This study contributes to the existing literature by offering a novel perspective on the role of linguistic attributes in fake news detection.