Learning review representations from user and product level information for spam detection

TL;DR

This paper introduces HFAN, a hierarchical attention model that leverages user and product information to improve review semantics understanding for spam detection, significantly outperforming previous methods.

Contribution

The paper proposes a novel Hierarchical Fusion Attention Network that integrates user and product data into review analysis, capturing complex semantics for better spam detection.

Findings

Over 10% absolute precision improvement on four datasets

Effective integration of user and product information enhances review semantics

Model demonstrates versatility across different real-world datasets

Abstract

Opinion spam has become a widespread problem in social media, where hired spammers write deceptive reviews to promote or demote products to mislead the consumers for profit or fame. Existing works mainly focus on manually designing discrete textual or behavior features, which cannot capture complex semantics of reviews. Although recent works apply deep learning methods to learn review-level semantic features, their models ignore the impact of the user-level and product-level information on learning review semantics and the inherent user-review-product relationship information. In this paper, we propose a Hierarchical Fusion Attention Network (HFAN) to automatically learn the semantics of reviews from the user and product level. Specifically, we design a multi-attention unit to extract user(product)-related review information. Then, we use orthogonal decomposition and fusion attention to learn a user, review, and product representation from the review information. Finally, we take the review as a relation between user and product entity and apply TransH to jointly encode this relationship into review representation. Experimental results obtained more than 10\% absolute precision improvement over the state-of-the-art performances on four real-world datasets, which show the effectiveness and versatility of the model.