Machine Learning Methodologies for Predicting Fake News on Social Media X: A Comparative Investigation Over TruthSeeker Dataset

In this era of social media, where every piece of information is at the user's fingertips, anyone who has smart devices and an internet connection can access or create a new piece of information online on these social media sites with just a single click. This changes the delivery of news and how people stay informed. These days, individuals can receive breaking reports on their preferred social media sites without relying on traditional news broadcasting methods. This changes the method by which news is accessed and communicated in the modern internet era. But with this new way of getting information from social media platforms comes the new challenge of determining the credibility and accuracy of the information shared. Most of the time, fake news without proper fact-finding and misinformation can spread quickly, causing confusion and distrust among the public. This kind of fraudulent story is perhaps the foremost risk to any government and may cause social unrest and instability. In this research, our goal is to analyze and predict fraudulent news identification over digital platforms, especially the ‘X’, also known as 'Twitter, to create a reliable and efficient system that can identify fake news in real-time with high accuracy using machine learning algorithms on the CIC's (Canadian Institute for Cybersecurity's) truth seeker database of 2023. The proposed system is able to recognize fraudulent information across social platforms with its highest 99.97^% accuracy using LR and MLP machine learning algorithms, which is much higher as compared to the existing benchmark studies. Further, this proposed system helps to ensure the detection of bogus headlines over various digital media platforms with the highest accuracy, which minimizes the impact of misleading information and safeguards users from being deceived, which helps us to curb the spread of fraudulent breaking news and prevent potential harm to society.