Taming Misinformation: Fake Review Detection on Social Media platform using Hybrid Ensemble Technique

Abstract

In today's digital world, we witness exponential growth in the generation of textual content on a daily basis. However, the widespread dissemination of information through social media, online forums, and news websites has given rise to the proliferation of fake views, opinions, and reviews, posing a significant challenge in the battle against misinformation and manipulation. Machine Learning has become increasingly integral to real-world online activities, particularly in the area of Artificial Intelligence. Traditional methods often struggle to keep pace with the relentless creation of internet data. Consequently, short text processing has emerged as a new domain for the application of Machine Learning. This is where sentiment analysis come to the forefront, offering potent tools for discerning the authenticity of online content. Detecting and combating these fabricated sentiments are crucial for preserving the integrity of information and ensuring informed decision-making. This work focus on the previously unexplored area of user comments on review data. By leveraging N-gram technique and hybrid ensemble classification approaches, the research addresses critical issues in fake reviews classification, and sentiment analysis. The aim of this work is to detect fake reviews with limited text using NLP feature extraction, and hybrid ensemble classification algorithms, ultimately contributing to the enhancement of information integrity and decision-making in the digital age.