A Decision Support System for Detecting Low-quality Articles in Content Farms

Abstract

Recently, content farms (websites) have drawn considerable attentions as well as critics, because their main business models are focused on supplying low quality articles, rather than high quality ones, to the Internet. In this study, we show that such low quality articles may include fake articles, advertorial articles, and plagiarized articles. Further, we propose a decision support system based on integrating multiple machine learning approaches to detect low-quality articles from content farms.