Social Media Fake news detection Utilizing a variety of datasets with probabilistic latent semantic analysis & k-means algorithms
TL;DR
The proposed FNDHKM approach showed significantly high accuracy with low overhead, and the highest accuracy achieved was 85% and 90% for precision and accuracy, respectively, while the proposed FNDHKM approach showed significantly high accuracy with low overhead.
Abstract
Social media has become usefull and most important platform for individuals to access news due to its speed and cost-effectiveness of disseminating information on a particular channel. However, these platforms also make it a breeding ground for the spread of fake news, which can impact society and individuals. With the advent of modern technology that comes with the fourth industrial revolution, promoting openness and increased engagement, social media has evolved to serve multiple purposes. It has become an integral part of our lives, beyond what was originally intended for just a small group of people. Consequently, identifying such fake news has become a crucial task for researchers and remains a major concern. This paper aims to examine the methods of publishing and distributing fake news, and outlines the approach of classifying, organizing, and developing algorithms. Our proposed solution is called the "Fake News Detection using Hybrid-kMeans (FNDHKM)" algorithm that utilizes Natural Language Processing (NLP) and machine learning (ML) techniques to detect fake news on social media platforms, specifically Twitter. The experiments were performed on three different datasets obtained from Twitter and involved dimensionality reduction on the extracted data. The highest accuracy achieved was 85% and 90% for precision and accuracy, respectively. The proposed FNDHKM approach showed significantly high accuracy with low overhead.