Spot Fake: Exposing Fake News Using Machine Learning (International Journal of Food and Nutritional Sciences, 2024)
With the constant barrage of information and digital connectivity, the proliferation of fake news poses a significant threat to societal discourse and decision-making. The spread of unverified information online has become a major threat to critical societal issue, influencing public opinion, political discourse, and even public safety. To address this challenge, this paper introduces SPOT FAKE, Developing a more effective method for fake news detection using machine learning. Leveraging the power of the Support Vector Machine (SVM) algorithm, SPOT FAKE aims to improve the detection of fake news identification. By analyzing linguistic cues, semantic features including analysis of the surrounding information within news pieces, SPOT FAKE seeks to distinguish between genuine and deceptive content, thereby empowering users to make informed decisions and combat the spread of misinformation. Through rigorous experimentation and evaluation, the efficacy of SPOT FAKE is demonstrated, highlighting its potential to contribute to the preservation of information integrity in the digital age