Web Robot Detection in Academic Publishing

TL;DR

This paper presents a supervised learning approach to detect web robots in academic publishing websites, emphasizing the importance of semantic features derived from LDA analysis to improve detection accuracy.

Contribution

The study introduces novel semantic features using LDA in web robot detection, demonstrating their effectiveness in a real-world academic publishing context.

Findings

Semantic features significantly improve detection accuracy

Supervised algorithms effectively distinguish human users from robots

Semantic analysis is crucial for reliable web robot detection

Abstract

Recent industry reports assure the rise of web robots which comprise more than half of the total web traffic. They not only threaten the security, privacy and efficiency of the web but they also distort analytics and metrics, doubting the veracity of the information being promoted. In the academic publishing domain, this can cause articles to be faulty presented as prominent and influential. In this paper, we present our approach on detecting web robots in academic publishing websites. We use different supervised learning algorithms with a variety of characteristics deriving from both the log files of the server and the content served by the website. Our approach relies on the assumption that human users will be interested in specific domains or articles, while web robots crawl a web library incoherently. We experiment with features adopted in previous studies with the addition of novel semantic characteristics which derive after performing a semantic analysis using the Latent Dirichlet Allocation (LDA) algorithm. Our real-world case study shows promising results, pinpointing the significance of semantic features in the web robot detection problem.