Abstract
As various review sites grow in popularity and begin to hold more sway in consumer preferences, spam detection has become a burgeoning field of research. While there have been various attempts to resolve the issue of spam on the open web, specifically as it relates to reviews, there does not yet exist an adaptive and robust framework out there today. We attempt to address this issue in a domain-specific manner, choosing to apply it to Yelp.com first. We believe that while certain processes do exist to filter out spam reviews for Yelp, we have a comprehensive framework that can be extended to other applications of spam detection as well. Furthermore, our framework exhibited a robust performance even when trained on small datasets, providing an approach for practitioners to conduct spam detection when the available data is inadequate. To the best of our knowledge, our framework uses the most number of extracted features and models in order to finely tune our results. In this paper, we will show how various sets of online review features add value to the final performance of our proposed framework, as well as how different machine learning models perform regarding detecting spam reviews.
Highlights
As information media comes to play more prominent roles in modern society, people’s daily lives have become more and more inseparable from, as well as susceptible to, the dispersion of information on media platforms
Using the framework we proposed, a close experimental study will be conducted on detecting Yelp spam reviews
PROCEDURE FOR PAPER SUBMISSION Our framework is divided into two major pipelines, which consist of a feature extraction component as well as a predictive modeling component
Summary
As information media comes to play more prominent roles in modern society, people’s daily lives have become more and more inseparable from, as well as susceptible to, the dispersion of information on media platforms. Despite efforts in the past on combating spam posts and comments online through various means, the patterns for online spam are evolving so rapidly that few spam detection algorithms are robust and adaptive enough to cope. In this project, we aim to design an adaptive and robust online spam detection framework that combines many cutting-edge techniques related to feature extraction, feature engineering, as well as machine learning algorithms - tree-based models, neural networks, statistical models, and so on. Within the framework we propose, we will implement different predictive algorithms and compare their performance on our experimental dataset. We will train our framework on smaller datasets to exhibit the robustness of our framework
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